Africa
Paralinguistics-A ware Speech-Empowered Large Language Models for Natural Conversation
Recent work shows promising results in expanding the capabilities of large language models (LLM) to directly understand and synthesize speech.
Self-Guiding Exploration for Combinatorial Problems
They utilize prompting techniques such as Exploration-of-Thought, Decomposition, and Refinement to effectively navigate and solve intricate tasks.
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.
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.
Geo-Diverse Safety Alignment Da Yin
Content W arning: This paper may contain examples of harmful contents by nature.
CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
The objective of dataset condensation is to ensure that the model trained with the synthetic dataset can perform comparably to the model trained with full datasets. However, existing methods predominantly concentrate on classification tasks, posing challenges in their adaptation to time series forecasting (TS-forecasting).