Deep Learning
Deep Learning for Computing Convergence Rates of Markov Chains
Markovian models, estimating the rate of convergence to equilibrium is critical.
Supplementary Materials for M
The attribute extractions of these notes were then verified for by authors of this paper.
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space Core Francisco Park
To address these questions, we propose analyzing a model's learning
Hyper-opinion Evidential Deep Learning for Out-of-Distribution Detection
Evidential Deep Learning (EDL), grounded in Evidence Theory and Subjective Logic (SL), provides a robust framework to estimate uncertainty for out-of-distribution (OOD) detection alongside traditional classification probabilities.
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development.
Enhancing Efficiency of Generated Code via Self-Optimization Dong Huang
These profiles are fed back to the LLM, which then revises the code to reduce overhead.
FAST opic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model Xiaobao Wu1 Thong Nguyen
Topic models have been evolving rapidly over the years, from conventional to recent neural models.
Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals
Electrocardiogram (ECG) signals provide essential information about the heart's