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Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models
In this paper we mainly focus on classification and forecasting tasks.
Most Influential Subset Selection: Challenges, Promises, and Beyond Y uzheng Hu
Subset Selection (MISS) problem, which aims to identify a subset of training samples with the greatest collective influence.
Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies
Diffusion models (DMs) have emerged as a promising approach for behavior cloning (BC).
Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions Heng Li
Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions. However, current VLN frameworks often rely on static environments and optimal expert supervision, limiting their real-world applicability. To address this, we introduce Human-Aware Vision-and-Language Navigation (HA-VLN), extending traditional VLN by incorporating dynamic human activities and relaxing key assumptions.
STL: Still Tricky Logic (for System Validation, Even When Showing Y our Work) Isabelle Hurley
Previous work showed that despite claims of interpretability, humans are unable to use formal specifications presented in a variety of ways to validate even simple robot behaviors.
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice.
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
Accurately predicting conditional average treatment effects (CA TEs) is crucial in personalized medicine and digital platform analytics.
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates
Public LLMs such as the Llama 2-Chat underwent alignment training and were considered safe. Recently Qi et al. [2024] reported that even benign fine-tuning on seemingly safe datasets can give rise to unsafe behaviors in the models.
Large Language Model Unlearning via Embedding-Corrupted Prompts
Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget.