10 accepted papers
Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversa…
Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoint. In this paper, we ask: Can a unified principle address all three? Such unification could simplify ML pipelines and f…
Diffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate weight checkpoints are not fully utilized and only the last converged checkpoint is used. In this work, we find proper che…
Large language models (LLMs) excel in generating coherent text, but they often struggle with context awareness, leading to inaccuracies in tasks requiring faithful adherence to provided information. We introduce FastMem, a novel method designed to enhance instruction fine-tuned LLMs’ context awarene…
Despite recent exponential advancements in computer vision and reinforcement learning, it remains challenging for robots to interact with liquids. These challenges are particularly pronounced due to the limitations imposed by opaque containers, transparent liquids, fine-grained splashes, and visual…
In this era the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However in the realm of 3D vision while remarkable progress has been made with models trained on large-scale synthetic and real-captured object data like Objavers…
Deep neural networks have achieved remarkable results across various tasks. However, they are susceptible to adversarial examples, which are generated by adding adversarial perturbations to original data. Adversarial training (AT) is the most effective defense mechanism against adversarial examples…
Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients could be non-identically distributed and have different sizes. Personalized Federated Learning (PFL) attempts to solve t…
In Federated Learning (FL) and many other distributed training frameworks, collaborators can hold their private data locally and only share the network weights trained with the local data after multiple iterations. Gradient inversion is a family of privacy attacks that recovers data from its generat…
Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distributed data. Many such frameworks only ask collaborators to share their local update of a common model, i.e. gradients w…