Meet up with Cohere and learn from top NLP experts at industry and research conferences, webinars, and hackathons. 

Events - AI Conferences, NLP Webinars, LLM Hackathons | Cohere

Events

About the session: This session will cover the following papers:

Cutting-edge diffusion models produce high-quality and customizable images, enabling their use for commercial art and graphic design purposes. First, I will discuss our study of various frameworks to detect replication. Then, I will show how we used these frameworks to identify memorization in the Stable Diffusion 1.4 model. In the second part, I will discuss various factors contributing to memorization in diffusion models. While it is widely believed that duplicated images in the training set are responsible for content replication at inference time, I will show results on how the text conditioning of the model also plays an important role. I will discuss several techniques we proposed based on these findings for reducing data replication in both training and inference times. Lastly, I will discuss my recent work on understanding style memorization in diffusion models where we propose a feature extractor for representing style, which we then use to detect style in Stable Diffusion generated images.

 Gowthami Somepalli is a Ph.D. candidate in the Department of Computer Science at the University of Maryland, College Park. The focus of her research lies at the intersection of Machine Learning (ML) and Computer Vision with the aim of building practical ML systems that are interpretable and robust. Her recent works range from developing novel deep learning architectures and training methodologies for diverse domains such as tables, images, and language, to building tools to explain and analyze the success/failure modes of common deep learning models. Before starting her Ph.D., she worked in industry in various product and engineering roles and received her bachelor's from IIT Madras. She is also the recipient of the Kulkarni Fellowship, Amazon Research Fellowship, CVPR Doctoral Consortium Award, and Ann-Wylie Dissertation Fellowship.

Cohere For AI - Guest Speaker: Gowthami Somepalli, Ph.D candidate

Cohere for AI -  Gowthami Somepalli - Understanding and Mitigating Memorization in Diffusion Models

This is your chance to dive into the power of Cohere's cutting-edge, large language models, Command R and Command R+. Learn how these can be leveraged with Cohere Embed and Rerank and start building enterprise-grade Knowledge Agents!

During this interactive event, you'll have the unique opportunity to:


Get hands-on experience with Cohere mentors and understand how these models can revolutionize your development projects.

Learn from Cohere's experts, who will guide you through the ins and outs of our technology and share best practices for building impactful applications.

Connect with fellow developers and expand your network, fostering potential collaborations and knowledge-sharing opportunities.


These new models are a family of highly scalable language models that balance high performance with strong accuracy. Command R and Command R+ are designed to offer best-in-class Augmented Retrieval (RAG) capabilities, multilingual coverage in 10 key business languages, and Tool Use to automate sophisticated workflows. 

Learn more 

Limited spots are available. Submit your interest to attend (Register Here). Successful applicants will receive a confirmation email. 

Cohere Build Day | London

Register today to have a chance to attend Cohere Build Day in London!

Cohere Build Day | San Francisco 

Register today to have a chance to attend Cohere Build Day in San Francisco!

Short Bio: Hasan is a PhD student at King Abdullah Univesity of Science and Technology (KAUST) under the supervision of Bernard Ghanem. Hasan has worked in multi topics such as robustness, 3D understanding, LLM agents, and continual learning. The work to be presented in this session is on data diversity and self-supervised training which was completed during Hasan's internship at The University of Oxford.

Abstract: We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downstream data is minimal. Notably, even with an exceptionally large pretraining data diversity achieved through methods like web crawling or diffusion-generated data, among other ways, the distribution shift remains a challenge. Our experiments are comprehensive with seven SSL methods using large-scale datasets such as ImageNet and YFCC100M amounting to over 200 GPU days. Code and trained models will be available at 

https://github.com/hammoudhasan/DiversitySSL

Cohere For AI - Guest Speaker:Hasan Abed Al Kader Hammoud, PhD Student

Cohere for AI - Hasan Abed Al Kader Hammoud - On Pretraining Data Diversity for Self-Supervised Learning

Bio: Ravi Mangal is a postdoctoral researcher at Carnegie Mellon University in the Security and Privacy Institute (CyLab). He graduated with a PhD in Computer Science from Georgia Institute of Technology in 2020. He is interested in all aspects of designing and applying formal methods for assuring the correctness and safety of software systems. His current research focuses on developing algorithms and methodologies for formally analyzing the safety and trustworthiness of learning-enabled systems.

Link to paper: "Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study" 

Cohere For AI - Guest Speaker: Ravi Mangal, Researcher at CMU Cylab

Cohere for AI - Ravi Mangal - Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study 

While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard $A^$ search. Searchformer is an encoder-decoder Transformer model trained to predict the search dynamics of $A^$. This model is then fine-tuned via expert iterations to perform fewer search steps than $A^$ search while still generating an optimal plan. In our training method, $A^*$'s search dynamics are expressed as a token sequence outlining when task states are added and removed into the search tree during symbolic planning. In our ablation studies on maze navigation, we find that Searchformer significantly outperforms baselines that predict the optimal plan directly with a 5-10$\times$ smaller model size and a 10$\times$ smaller training dataset. We also demonstrate how Searchformer scales to larger and more complex decision making tasks like Sokoban with improved percentage of solved tasks and shortened search dynamics.



 Lucas Lehnert is a postdoctoral researcher as part of the FAIR team at Meta. Lucas' research focuses on the underlying computational principles of intelligent decision making and how intelligent agents can generalize and re-use previously learned knowledge. Before joining Meta, he worked at the Mila Quebec AI Institute and the Université de Montréal. In 2021 Lucas completed his PhD at Brown University in Computer Science and before that his MSc and BSc in Computer Science at McGill University. His interdisciplinary work was recognized by an NIH training grant in interactionist cognitive neuroscience and a best student workshop paper award. Dr. Lucas Lehnert will be joining Computer Science department at the University of Saskatchewan as an Assistant Professor in July 2024.

Cohere For AI - Guest Speaker: Lucas Lehnert, Meta AI FAIR

Cohere for AI - Lucas Lehnert - Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Description: Floods are the most common natural disaster, directly affecting billions of people with a disproportionate impact in developing countries. Early warning systems allow people time to prepare for floods, saving lives and property. A major challenge is a lack of streamflow data -- hydrological models must be calibrated using data records in each location where they are used for forecasting. Machine learning allows us to train a model using all streamflow data available globally, resulting in a model that can be applied anywhere. This allows us to provide significantly more accurate flood forecasts for locations in the world that lack observation records.

Cohere For AI - Guest Speaker: Grey Nearing, Sr. Research Scientist

Cohere for AI - Grey Nearing - Flood Forecasting at Google

Cohere is thrilled to announce that we will be a premium sponsor at Microsoft Build 2024! As a leading AI company, we are excited to showcase our latest innovations and connect with the developer community. Stay tuned for more updates as we get closer to the event. In the meantime, mark your calendars and get ready for an exciting and insightful experience at Microsoft Build 2024. 

Microsoft Build 

Cohere will be a Premium Sponsor at Microsoft Build 

 Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision. However, the interpretability of these models remains a challenge. To address this, we propose LeGrad, an explainability method specifically designed for ViTs. LeGrad computes the gradient with respect to the attention maps of ViT layers, considering the gradient itself as the explainability signal. We aggregate the signal over all layers, combining the activations of the last as well as intermediate tokens to produce the merged explainability map. This makes LeGrad a conceptually simple and an easy-to-implement tool for enhancing the transparency of ViTs. We evaluate LeGrad in challenging segmentation, perturbation, and open-vocabulary settings, showcasing its versatility compared to other SotA explainability methods demonstrating its superior spatial fidelity and robustness to perturbations.



 I'm a PhD student at Bonn University, advised by Prof. Hilde Kuehne. I'm also participating in MIT-IBM Watson Sight and Sound Project.My primary research area is deep learning for multimodal models. Particularly, I am interested in zero-shot adaptation of pretrained models for emerging behavior.Prior to this, I finished my Master of Engineering in Applied Mathematics at ENSTA Paris in France and my Master of Science in Statistics and applied Probabilities at the National University of Singapore (NUS).

Cohere For AI - Guest Speaker: Walid Bousselham, PhD Student

Cohere for AI -  Walid Bousselham - LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity

 AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models. \textsc{Proximal Policy Optimization} (PPO) has been positioned by recent literature as the canonical method for the RL part of RLHF. However, it involves both high computational cost and sensitive hyperparameter tuning. We posit that most of the motivational principles that led to the development of PPO are less of a practical concern in RLHF and advocate for a less computationally expensive method that preserves and even increases performance. We revisit the \textit{formulation} of alignment from human preferences in the context of RL. Keeping simplicity as a guiding principle, we show that many components of PPO are unnecessary in an RLHF context and that far simpler REINFORCE-style optimization variants outperform both PPO and newly proposed "RL-free" methods such as DPO and RAFT. Our work suggests that careful adaptation to LLMs alignment characteristics enables benefiting from online RL optimization at low cost.



Arash is currently a Member of Technical staff at Cohere where he is a part of the RL Research team. He was previously part of C4AI Scholar Program, where he worked on RLHF and alignment, post-training quantization at scale, and Mixture of Experts. Prior to that, he was a researcher at the Vector Institute working on model-based RL and LLMs. He also spent some time at Cerebras working on compiler optimizations for their wafer scale deep learning accelerator.

Cohere For AI - Guest Speaker:Arash Ahmadian, Technical staff @ Cohere

Cohere for AI -  Arash Ahmadian - Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Gohar is currently pursuing his Master's degree in Electrical and Computer Engineering at the University of Alberta, under the guidance of Dr. Li Cheng and Dr. Xingyu Li. His research focuses on applying Generative AI and Reinforcement Learning for 3D digital human motion control. With a solid background that includes two years of industry experience in Machine Learning and Computer Vision at Alta ML, Teradata and Hotpot.AI, Gohar has worked on projects involving Image Matting, Image Inpainting, and 3D Fault Detection. Prior to his Master's, he earned his Bachelor's degree in Electrical Engineering from the National University of Sciences and Technology, where he explored Neural Network Acceleration for Embedded Hardware.



 We introduce MoMask, a novel masked modeling framework for text-driven 3D human motion generation. In MoMask, a hierarchical quantization scheme is employed to represent human motion as multi-layer discrete motion tokens with high-fidelity details. Starting at the base layer, with a sequence of motion tokens obtained by vector quantization, the residual tokens of increasing orders are derived and stored at the subsequent layers of the hierarchy. This is consequently followed by two distinct bidirectional transformers. For the base-layer motion tokens, a Masked Transformer is designated to predict randomly masked motion tokens conditioned on text input at training stage. During generation (i.e. inference) stage, starting from an empty sequence, our Masked Transformer iteratively fills up the missing tokens; Subsequently, a Residual Transformer learns to progressively predict the next-layer tokens based on the results from current layer. Extensive experiments demonstrate that MoMask outperforms the state-of-art methods on the text-to-motion generation task, with an FID of 0.045 (vs e.g. 0.141 of T2M-GPT) on the HumanML3D dataset, and 0.228 (vs 0.514) on KIT-ML, respectively. MoMask can also be seamlessly applied in related tasks without further model fine-tuning, such as text-guided temporal inpainting. abs: 

Cohere For AI - Guest Speaker: Muhammad Gohar Javed, Master's Student

Cohere for AI - Muhammad Gohar Javed - MoMask: Generative Masked Modeling of 3D Human Motions (CVPR 2024)

Abstract: In this talk, I will share our progress on developing algorithms that allow agents to learn continually and in real-time on edge devices.

About the speaker: I am a PhD student at the RLAI lab at University of Alberta advised by Prof. Rich Sutton. I'm interested in building algorithms for real-time continual learning for achieving goals in big worlds. In the past, I did my M.Sc with Prof. Martha White on Meta-learning representations for Continual learning, worked at MILA with Prof. Yoshua Bengio on Causality and at TUKL-SEECS with Prof. Faisal Shafait on document analysis and continual learning. I also represented my home country at 55th International Mathematical Olympiad , and XXVI Asian Pacific Mathematical Olympiad, receiving an honorable-mention and a bronze medal respectively.

Cohere For AI - Guest Speaker: Khurram Javed, Ph.D Student

Cohere for AI -  Khurram Javed - Real-time Reinforcement Learning using Dynamic Networks(

In this presentation, we will delve into the challenges and solutions surrounding cloud segmentation in remote sensing, focusing on the innovative approach introduced in the paper "Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding." We will begin by outlining the problem statement, highlighting the limitations of traditional cloud segmentation methods, particularly their struggle with inaccurate labeling in training data, which leads to suboptimal model performance.
Next, we will briefly touch upon traditional techniques used in cloud segmentation, acknowledging their reliance on pre-existing knowledge and the distinction between Earth's surface and clouds, as well as their vulnerability to intricate backgrounds and the subjective nature of threshold selection.



Following this, we will discuss the challenges these traditional techniques face, emphasizing their inability to effectively distinguish clouds in the presence of atmospheric aerosols, dust, smoke, and hazy pixels, and their dependence on the availability of accurate mask annotations, which are often invalid.

Finally, we will introduce Cloud Adaptive-Labeling (CAL), a groundbreaking method that addresses these challenges by iteratively improving the quality of training data annotations through a trainable pixel intensity threshold. This dynamic thresholding strategy adapts to changes in training loss, enhancing the model's ability to accurately distinguish clouds from other elements in remote sensing imagery. Through extensive experiments, CAL has demonstrated significant improvements in model performance, surpassing existing techniques and establishing new state-of-the-art results.

Cohere For AI - Guest Speaker: Jay Gala, Generative AI Engineer 

Cohere for AI - Jay Gala - Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding 

Visual object tracking, a key challenge in computer vision, is crucial for analyzing athlete performance in videos. This talk explores our experience applying such algorithms to track skiers. Skiing poses unique challenges: athletes in motion with variable equipment, high speeds, and constantly changing terrain in harsh winter conditions. Further, large ski courses necessitate multiple cameras with transitions, creating videos with cuts. We'll evaluate how existing tracking algorithms perform under these conditions and explore how tracking data can be used for high-level analysis of skiing trajectories, both in real-world scenarios and with dedicated hardware.


The associated paper for the session: 

Cohere For AI - Guest Speaker: Matteo Dunnhofer, Postdoc Researcher

Cohere for AI -  Matteo Dunnhofer - Leveraging Visual Object Tracking on the Slopes

In cloud-based gaming and virtual reality (G&VR), scene content is rendered in a cloud server and streamed as low-latency encoded video to the client device. In this context, distributed rendering aims to offload parts of the rendering to the client. An adaptive approach is proposed, which dynamically assigns assets to client-side vs. server-side rendering according to varying rendering time and bitrate targets.
This is achieved by streaming perceptually-optimized scene control weights to the client, which are compressed with a composable autoencoder in conjunction with select video segments. This creates an adaptive render-video (REVI) streaming framework, which allows for substantial tradeoffs between client rendering time and the bitrate required to stream visually-lossless video from the server to the client. In order to estimate and control the client rendering time and the required bitrate of each subset of each scene, a random-forest based regressor is proposed, in conjunction with the use of AIMD (additive-increase/multiplicative-decrease) to ensure predetermined average bitrate or render-time targets are met. Experiments are presented based on typical sets of G&VR scenes rendered in Blender and HEVC low-latency encoding. A key result is that, when the client is providing for 50% of the rendering time needed to render the whole scene, up to 60% average bitrate saving is achieved versus streaming the entire scene to the client as video.

Cohere For AI - Guest Speaker: Dr. Matthias Treder, ML Engineer 

Cohere for AI - Dr. Matthias Treder - Adaptive Render-Video Streaming for Virtual Environments

We will explore quality-diversity optimization to improve fairness across skin tones in facial recognition systems by sampling diverse, synthetic facial datasets from the generative StyleGAN2 model

Allen's Bio: Allen Chang is a 4th-year undergraduate at the University of Southern California (USC) working with Profs. Maja Matarić, Jesse Thomason, and Stefanos Nikolaidis. His current interests are language grounding, social inference and reasoning, and learning from synthetic data. Previously, he was an intern at Tsinghua University, MIT Haystack Observatory, and the CMU Robotics Institute.

The associated paper for the session is @ 

Cohere For AI - Guest Speaker: Allen Chang, Undergrad at USC Viterbi

Cohere for AI -  Allen Chang - Quality-Diversity Generative Sampling (QDGS) for Learning with Synthetic Data

Abstract: We present an approach to pose object recognition as next token prediction. The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels. To ground this prediction process in auto-regression, we customize a non-causal attention mask for the decoder, incorporating two key features: modeling tokens from different labels to be independent, and treating image tokens as a prefix. This masking mechanism inspires an efficient method - one-shot sampling - to simultaneously sample tokens of multiple labels in parallel and rank generated labels by their probabilities during inference. To further enhance the efficiency, we propose a simple strategy to construct a compact decoder by simply discarding the intermediate blocks of a pretrained language model. This approach yields a decoder that matches the full model's performance while being notably more efficient. abs: 

Cohere For AI - Guest Speaker: Kaiyu Yue, Student at Univ. of Maryland

Cohere for AI - Kaiyu Yue presents Object Recognition as Next Token Prediction

Aidan Gomez, Co-founder and CEO at Cohere will speak on the main stage at Collision in Toronto. Check back for more details on the session. 

Collision 

Announcement: Aidan Gomez will be speaking at Collision

About the speaker:"I am Hongyu Wang (王鸿钰 in Chinese), a second-year Ph.D candidate at VIPL group, Chinese Academy of Sciences (CAS) under the supervision of Professor Xilin Chen and Professor Ruiping Wang. I received my B.Eng. degree from Department of Computer Science and Technology, University of Science and Technology of China, where I was advised by associate researcher Chao Qian. I was a research intern under the supervision of Dr. Furu Wei and Shuming Ma at Natural Language Computing group, MSR-Asia from Aug. 2021 to Jan. 2023. I have great instreast on the efficient architecture for the large-scale foundation models and multimodal reasoning."

Session Description: Recent research, such as BitNet, is paving the way for a new era of 1- bit Large Language Models (LLMs). In this talk, we will share our latest progress on extremely low-bit large language models, a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.

Cohere For AI - Guest Speaker: Hongyu Wang Ph.D candidate @ VIPL group

Cohere for AI -  Hongyu Wang - The Era of 1-bit LLMs 

This session unveils a novel text-based method for 3D content editing, utilizing 2D model semantics for intuitive shape localization and texture mapping. By forgoing extensive 3D datasets and using neural optimization with pre-trained 2D models, we will explore how the technique enhances texture and detail, providing unparalleled control over 3D modifications through simple text instructions.

Dales's Bio: Dale is a PhD student at the University of Chicago’s 3DL (threedle) lab (

) studying computer graphics, 3D computer vision, and deep learning. Dale is advised by Rana Hanocka.
The associated papers for the session are @ 

Cohere For AI - Guest Speaker: Dale Decatur, PhD student

Cohere for AI -  Dale Decatur - Text-driven Localization and Local Editing of 3D Shapes

Explore current research trends using synthetic data for training. Discover G-NoCL, an online continual learning framework based on category-only data generation. Additionally, learn about DISCOBER, a novel data sampling method enhancing performance over traditional web-supervised and manually annotated approaches. (

)

Diganta's Bio: Diganta is a UNIQUE Scholar Research MSc at Mila, Montreal supervised by Prof Irina Rish within the CERC AAI Lab and is a Visiting Researcher at the Human Sensing Lab at Carnegie Mellon University. He is also the founder of a non-profit research organisation called Landskape AI. Previously he has worked as researcher and engineer at firms like Weights & Biases and Morgan Stanley. His current research interests spans topics of constrained learning - sparsity, mixture of experts, lifelong learning, generative models, learning dynamics and code generation. Diganta is also an incoming ELLIS PhD Fellow at the Max Planck Institute in Tübingen. Beyond research, he is an avid football, F1 and e-sport follower.

Cohere For AI - Guest Speaker: Diganta Misra, Research Scholar 

Cohere for AI - Diganta Misra - Unleashing the Power of Generated Data in Lifelong Learning!

Self-supervised learning has unlocked the potential of scaling up pretraining to billions of images, since annotation is unnecessary. But are we making the best use of data? How more economical can we be? In this work, we attempt to answer this question by making two contributions. First, we investigate first-person videos and introduce a "Walking Tours" dataset. These videos are high-resolution, hours-long, captured in a single uninterrupted take, depicting a large number of objects and actions with natural scene transitions. They are unlabeled and uncurated, thus realistic for self-supervision and comparable with human learning.Second, we introduce a novel self-supervised image pretraining method tailored for learning from continuous videos. Existing methods typically adapt image-based pretraining approaches to incorporate more frames. Instead, we advocate a "tracking to learn to recognize" approach. Our method called DoRA, leads to attention maps that Discover and tRAck objects over time in an end-to-end manner, using transformer cross-attention. We derive multiple views from the tracks and use them in a classical self-supervised distillation loss. Using our novel approach, a single Walking Tours video remarkably becomes a strong competitor to ImageNet for several image and video downstream tasks.



 Shashanka Venkataramanan is a final year doctoral student with the LinkMedia team at INRIA, where he is advised by Dr. Yannis Avrithis, Dr. Ewa Kijak and Dr. Laurent Amsaleg. His work mainly focuses on developing algorithms to enhance instance and category-level visual representations in the supervised and self-supervised settings.

Cohere For AI - Guest Speaker: Shashanka Venkataramanan, PhD at INRIA

Cohere for AI - Shashanka Venkataramanan - Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video

The discussion will explore Common-Sense Bias Discovery (CSBD), a novel method leveraging image descriptions to identify and mitigate biases in image classification by adjusting sampling weights, significantly outperforming current bias mitigation techniques without needing sensitive labels.

The associated paper for the session is @ 



Miao's Bio: Miao is a a CS Ph.D. student at New York University (NYU) and a Graduate Researcher at the The Visualization and Data Analytics Research Center at NYU. (

) Her research direction lies at the intersection of computer vision and fairness/bias mitigation, applied on real-world environmental and healthcare problems.

Cohere For AI - Guest Speaker: Miao Zhang, CS Ph.D. student at NYU

Cohere for AI - Miao Zhang - Common-Sense Bias Discovery and Mitigation for Classification Tasks

The discussion will explore training CLIP models with synthetic data, focusing on the paper's methodologies, their effectiveness versus real data, and future research and application impacts in AI and computer vision.

The associated paper for the session is @ 



Fabio's Bio: Fabio is a postdoctoral researcher in the Torr Vision Group (

) at the University of Oxford, working with Phil Torr. Previously, Fabio was a co-tutelle PhD at Vislab/Ambarella, located in Parma (Italy) and Inria Astra-vision team, in Paris (France), where he was supervised by Raoul de Charette. Fabio is currently working on large scale diffusion models for image generation, and continual learning.

Cohere For AI - Guest Speaker: Fabio Pizzati,Researcher Univ of Oxford

Cohere for AI - [C4AI] Fabio Pizzati - SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

Cohere For AI - Guest Speaker: Gowthami Somepalli, Ph.D candidate

Upcoming Events

Cohere’s past events