115 citations · 317 across the 36 of their papers we have counts for
9 papers · 1 filter
Understanding Optimization in Deep Learning with Central Flows
Jeremy M. Cohen, Alex Damian, Ameet Talwalkar +2
Traditional theories of optimization cannot describe the dynamics of optimization in deep learning, even in the simple setting of deterministic training. The challenge is that opti…
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian +11
The robustness of LLMs to jailbreak attacks, where users design prompts to circumvent safety measures and misuse model capabilities, has been studied primarily for LLMs acting as s…
Improving Alignment and Robustness with Circuit Breakers
Andy Zou, Long Phan, Justin Wang +7
AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interr…
From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers
Swaminathan Gurumurthy, Karnik Ram, Bingqing Chen +2
Various pose estimation and tracking problems in robotics can be decomposed into a correspondence estimation problem (often computed using a deep network) followed by a weighted le…
Why is SAM Robust to Label Noise?
Christina Baek, Zico Kolter, Aditi Raghunathan
Sharpness-Aware Minimization (SAM) is most known for achieving state-of the-art performances on natural image and language tasks. However, its most pronounced improvements (of tens…
Neural Network Verification with Branch-and-Bound for General Nonlinearities
Zhouxing Shi, Qirui Jin, Zico Kolter +3
Branch-and-bound (BaB) is among the most effective techniques for neural network (NN) verification. However, existing works on BaB for NN verification have mostly focused on NNs wi…