4 citations · 6 across the 6 of their papers we have counts for
6 papers
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition
Priya Kasimbeg, Frank Schneider, Runa Eschenhagen +11
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorit…
Sensitivity of Generative VLMs to Semantically and Lexically Altered Prompts
Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry +3
Despite the significant influx of prompt-tuning techniques for generative vision-language models (VLMs), it remains unclear how sensitive these models are to lexical and semantic a…
SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations
Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry +3
Despite their remarkable successes, state-of-the-art large language models (LLMs), including vision-and-language models (VLMs) and unimodal language models (ULMs), fail to understa…
VISLA Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations
Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry +3
Despite their remarkable successes, state-of-the-art language models face challenges in grasping certain important semantic details. This paper introduces the VISLA (Variance and I…
Test-Time Training for Depression Detection
Sri Harsha Dumpala, Chandramouli Shama Sastry, Rudolf Uher +1
Previous works on depression detection use datasets collected in similar environments to train and test the models. In practice, however, the train and test distributions cannot be…
Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
Yujia Huang, Adishree Ghatare, Yuanzhe Liu +6
We study the problem of symbolic music generation (e.g., generating piano rolls), with a technical focus on non-differentiable rule guidance. Musical rules are often expressed in s…