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.
Information Re-Organization Improves Reasoning in Large Language Models
Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest.
Simple and Effective Masked Diffusion Language Models
MDLM's objective correspond to a principled variational lower bound, and supports generation via
ฮฒ-DPO: Direct Preference Optimization with Dynamic ฮฒ Junkang Wu
Despite the effectiveness, RLHF's instability and computational requirements often limit its practical
A Functional Extension of Semi-Structured Networks
A significant challenge in this integration is maintaining the interpretability of the additive model component.
Geometry-aware training of factorized layers in tensor Tucker format
Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during training and inference phases.