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Barproda Halder's Projects

attnsleep icon attnsleep

The code for TNSRE work: "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"

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Cyclic Alternating Pattern

deepsleepnet icon deepsleepnet

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

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Official code of "Discover and Cure: Concept-aware Mitigation of Spurious Correlation" (ICML 2023)

eeg-dl icon eeg-dl

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

freeze-then-train icon freeze-then-train

Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise

group_dro icon group_dro

Distributionally robust neural networks for group shifts

lc icon lc

Official Implementation of Avoiding spurious correlations via logit correction

mvar-gauss-pid icon mvar-gauss-pid

Approximating deficiency-based PIDs for multivariate Gaussian systems

pid icon pid

[NeurIPS 2023, ICMI 2023] Quantifying & Modeling Multimodal Interactions

sap-1-v2-mojo icon sap-1-v2-mojo

SAP-1 CPU in Verilog for the Mojo FPGA board - has seperate address bus.

simplicitybiaspitfalls icon simplicitybiaspitfalls

The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)

torch_dcec icon torch_dcec

Pytorch Deep Clustering with Convolutional Autoencoders implementation

transunet icon transunet

This repo reproduces the results of TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

transunet-1 icon transunet-1

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

unetplusplus icon unetplusplus

Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

vit-pytorch icon vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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