Deep Learning
Probing the Decision Boundaries of In-context Learning in Large Language Models
Recent language models, such as GPT -3+ [Brown et al., 2020, Achiam et al., 2023], have demonstrated Recent attempts to understand in-context learning have focused on various aspects. On the practical side, research has investigated the impact of different factors on in-context learning.
Activation Map Compression through Tensor Decomposition for Deep Learning
The application of low-order decomposition results in considerable memory savings while preserving the features essential for learning, and also offers theoretical guarantees to convergence.
Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
The collected data in recommender systems generally suffers selection bias. Considerable works are proposed to address selection bias induced by observed user and item features, but they fail when hidden features (e.g., user age or salary) that affect
Online Adaptation of Language Models with a Memory of Amortized Contexts
However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention.
On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance
Although this problem is significant for safer and smarter driving systems, the exploration of this problem remains limited.
