AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Last update: Nov 26, 2022

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Deep Learning AT-BMC

Overview

AT-BMC

Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction (AAAI 2022) Paper

Prerequisites

Install packages by referring to pip_reqs.txt

Datasets

Movie Reviews (Paper: Pruthi et al. 2020, Weakly- and Semi-supervised Evidence Extraction, Findings of EMNLP)
MultiRC (Paper: Khashabi et al. 2018, Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences, NAACL-HLT)

Run

Adv Data Preparation

Please refer to augment_with_mask.py for data preparation. And the data folder contains the final used data.

Training

for seed in `seq 1 1`; do 
    CUDA_VISIBLE_DEVICES=$GPU_ID unbuffer python main.py \
        --data_dir datasets/movie_reviews_with_some_rats_adv \
        --batch_size 4 \
        --learning_rate 2e-5 \
        --max_seq_len 512 \
        --epochs 30 \
        --dataset multi_rc \
        --evaluate_every 10000 \
        --save_extraction_model ${OUTPUT_BASE_DIR}/ \
        --save_prediction_model ${OUTPUT_BASE_DIR}/ \
        --include_label_embedding_features \
        --seed $seed \
        --upper_case \
        --gradient_accumulation_steps 8  | tee -a logs/logs.txt
done;

AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Related tags

Overview

AT-BMC

Prerequisites

Datasets

Run

Adv Data Preparation

Training

Owner

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AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Related tags

Overview

AT-BMC

Prerequisites

Datasets

Run

Adv Data Preparation

Training

Owner

Draw like Bob Ross using the power of Neural Networks (With PyTorch)!

Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Self-Supervised depth kalilia

Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization

Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Protect against subdomain takeover

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation

NeurIPS-2021: Neural Auto-Curricula in Two-Player Zero-Sum Games.

Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy

Automatic Idiomatic Expression Detection

[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.

An Unsupervised Graph-based Toolbox for Fraud Detection

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.