Today we’re joined by Markus Nagel, research scientist at Qualcomm AI Research, who helps us kick off our coverage of NeurIPS 2023. In our conversation with Markus, we cover his accepted papers at the conference, along with other work presented by Qualcomm AI Research scientists. Markus’ first paper, Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing, focuses on tackling activation quantization issues introduced by the attention mechanism and how to solve them. We also discuss Pruning vs Quantization: Which is Better?, which focuses on comparing the effectiveness of these two methods in achieving model weight compression. Additional papers discussed focus on topics like using scalarization in multitask and multidomain learning to improve training and inference, using diffusion models for a sequence of state models and actions, applying geometric algebra with equivariance to transformers, and applying a deductive verification of chain of thought reasoning performed by LLMs.
The complete show notes for this episode can be found at twimlai.com/go/663.
GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681
Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680
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Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678
V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677
Video as a Universal Interface for AI Reasoning with Sherry Yang - #676
Assessing the Risks of Open AI Models with Sayash Kapoor - #675
OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674
Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673
Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - #672
Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671
AI Trends 2024: Reinforcement Learning in the Age of LLMs with Kamyar Azizzadenesheli - #670
Building and Deploying Real-World RAG Applications with Ram Sriharsha - #669
Nightshade: Data Poisoning to Fight Generative AI with Ben Zhao - #668
Learning Transformer Programs with Dan Friedman - #667
AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - #666
AI Trends 2024: Computer Vision with Naila Murray - #665
Are Vector DBs the Future Data Platform for AI? with Ed Anuff - #664
Responsible AI in the Generative Era with Michael Kearns - #662
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