AI Breakdown

arxiv Preprint - DoG is SGD’s Best Friend: A Parameter-Free Dynamic Step Size Schedule

In this episode we discuss DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule
by Maor Ivgi, Oliver Hinder, Yair Carmon. The paper presents a dynamic SGD step size formula called DoG that does not require manual tuning. The authors analyze the DoG formula and demonstrate its strong convergence guarantees for stochastic convex optimization. Empirical evaluation shows that DoG performs comparably to SGD with tuned learning rate and even outperforms tuned SGD in a per-layer variant.

CVPR 2023 - LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling

In this episode we discuss LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling
by Linjie Li, Zhe Gan, Kevin Lin, Chung-Ching Lin, Zicheng Liu, Ce Liu, Lijuan Wang. The paper presents LAVENDER, a unified video-language framework that uses Masked Language Modeling (MLM) as the common interface for pre-training and downstream tasks. LAVENDER simplifies the model architecture by using a lightweight MLM head on top of the multimodal encoder. Surprisingly, experimental results show that LAVENDER achieves competitive performance on various video-language benchmarks.

NeurIPS 2022 - Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

In this episode we discuss Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
by Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Derek Hoiem, Shih-Fu Chang, Mohit Bansal, Heng Ji. VidIL is a few-shot video-language learner that combines image and language models to generalize to different video-to-text tasks with limited examples. It translates video content into frame captions, object, attribute, and event phrases, and combines them into a temporal-aware template. The language model is then prompted with a few in-context examples to generate a target output. Experimental results show that VidIL outperforms supervised models on video future event prediction.

arxiv preprint - MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

In this episode we discuss MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action
by Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, Lijuan Wang. The paper introduces MM-REACT, a system that combines ChatGPT with expert vision models to tackle challenging visual tasks. MM-REACT utilizes a unique prompt design to enable language models to process multimodal information and interact with vision experts. Zero-shot experiments demonstrate the effectiveness of MM-REACT in achieving advanced visual understanding capabilities beyond existing models.

arxiv preprint - 3D-LLM: Injecting the 3D World into Large Language Models

In this episode we discuss 3D-LLM: Injecting the 3D World into Large Language Models
by Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, Chuang Gan. The paper proposes a new model called 3D-LLMs that integrates the 3D physical world into language models, allowing them to perform various 3D-related tasks such as captioning, question answering, and navigation. The authors employ three prompting mechanisms to collect a large dataset of 3D-language data efficiently and use a 3D feature extractor and 2D VLMs as the backbone for training the model. The experimental results demonstrate that the 3D-LLMs outperform existing baselines in terms of performance and capabilities.

arxiv preprint - Meta-Transformer: A Unified Framework for Multimodal Learning

In this episode we discuss Meta-Transformer: A Unified Framework for Multimodal Learning
by Yiyuan Zhang, Kaixiong Gong, Kaipeng Zhang, Hongsheng Li, Yu Qiao, Wanli Ouyang, Xiangyu Yue. The paper presents a framework called Meta-Transformer for processing multiple modalities in multimodal learning. It uses a frozen encoder for feature extraction across different modalities, including natural language, images, audio, and more. The Meta-Transformer framework demonstrates the potential of transformer architectures in achieving unified multimodal intelligence.

ICCV 2023 - Hidden Biases of End-to-End Driving Models

In this episode we discuss Hidden Biases of End-to-End Driving Models
by Bernhard Jaeger, Kashyap Chitta, Andreas Geiger. The paper discusses biases commonly found in state-of-the-art end-to-end driving systems, particularly in the context of CARLA. The first bias is a preference for target point following for lateral recovery, while the second bias involves averaging multimodal waypoint predictions for slowing down. The paper analyzes the drawbacks of these biases and proposes alternative approaches, leading to the development of TF++, a simple end-to-end method that outperforms prior work on Longest6 and LAV benchmarks.

arxiv preprint - Retentive Network: A Successor to Transformer for Large Language Models

In this episode we discuss Retentive Network: A Successor to Transformer for Large Language Models
by Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei. The paper introduces RETNET as a successor to the Transformer architecture for language models. RETNET utilizes a retention mechanism that supports parallel, recurrent, and chunkwise recurrent computation paradigms for efficient training and inference. Experimental results show that RETNET achieves favorable scaling, parallel training, low-cost deployment, and efficient inference, making it a promising candidate for large language models.

arxiv preprint - Challenges and Applications of Large Language Models

In this episode we discuss Challenges and Applications of Large Language Models
by Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, Robert McHardy. The paper provides a systematic analysis of the challenges and applications of Large Language Models (LLMs). In the Challenges section, it discusses obstacles such as dataset complexity, high training costs, and limited context length. The Applications section explores the use of LLMs in various domains, including chatbots, computational biology, and social sciences. The paper concludes by stressing the importance of understanding these challenges and applications to advance the field effectively.

ICML 2023 - Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains

In this episode we discuss Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains
by Vishwaraj Doshi, Jie Hu, Do Young Eun. This paper introduces self-repellent random walks (SRRWs) as a way to improve sampling efficiency in Markov chain Monte Carlo (MCMC) procedures. It proves that the SRRWs converge to the target distribution, provides a central limit theorem and covariance matrix, and shows that stronger repellence leads to smaller asymptotic covariance. The paper also demonstrates that the decrease in sampling variance for SRRW-driven MCMC algorithms is of the order O(1/α), where α controls the strength of repellence.