4 citations · 4 across the 6 of their papers we have counts for
6 papers
EEGPrep: a validated Python implementation of the EEGLAB preprocessing pipeline
Arnaud Delorme, Suraj Ranganath, Christian Kothe +3
Objective. Automated EEG preprocessing is common in research and clinical work, but few pipelines have been tested systematically. In a recent benchmark, the default EEGLAB pipelin…
EEGDash: An open-source platform for machine learning on public neurophysiological data
Bruno Aristimunha, Aviv Dotan, Pierre Guetschel +8
Public recordings of electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG (iEEG), functional near-infrared spectroscopy (fNIRS) and electromyography (EMG) a…
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…
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT
Aman Jaiswal, Evangelos Milios
Transformer-based models, specifically BERT, have propelled research in various NLP tasks. However, these models are limited to a maximum token limit of 512 tokens. Consequently, t…