Summary of Variance Control For Black Box Variational Inference Using the James-stein Estimator, by Dominic B. Dayta
Variance Control for Black Box Variational Inference Using The James-Stein Estimatorby Dominic B. DaytaFirst submitted…
Variance Control for Black Box Variational Inference Using The James-Stein Estimatorby Dominic B. DaytaFirst submitted…
Zero-shot LLM-guided Counterfactual Generation: A Case Study on NLP Model Evaluationby Amrita Bhattacharjee, Raha Moraffah,…
TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generationby Hritik Bansal, Yonatan Bitton, Michal Yarom, Idan Szpektor,…
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and…
Adapting WavLM for Speech Emotion Recognitionby Daria Diatlova, Anton Udalov, Vitalii Shutov, Egor SpirinFirst submitted…
Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuningby Karim Galliamov,…
Federated Reinforcement Learning with Constraint Heterogeneityby Hao Jin, Liangyu Zhang, Zhihua ZhangFirst submitted to arxiv…
Advancing Multimodal Medical Capabilities of Geminiby Lin Yang, Shawn Xu, Andrew Sellergren, Timo Kohlberger, Yuchen…
TED: Accelerate Model Training by Internal Generalizationby Jinying Xiao, Ping Li, Jie NieFirst submitted to…
Parameter-Efficient Fine-Tuning with Discrete Fourier Transformby Ziqi Gao, Qichao Wang, Aochuan Chen, Zijing Liu, Bingzhe…