AAAI-2021 paper: The Influence of Memory in Multi-Agent Consensus
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Updated
Mar 27, 2023 - Python
AAAI-2021 paper: The Influence of Memory in Multi-Agent Consensus
The L0-SIGN implementation.
AAAI 2021: A Multi-step-ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting
Description of the system and its results that we developed as a part of our participation at CONSTRAINT shared task in AAAI-2021.
The code for the paper "Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval" (AAAI'21)
Code for the submission to the ML Reproducibility Challenge 2022, reproducing "If you like Shapley then you'll love the core"
Experiments for the paper ´An experimental study of the Transferability of spectral graph networks´
A list of accepted papers in AAAI 2021 about anomaly detection.
Python implementation of Mercer Features for Efficient Combinatorial Bayesian Optimization
This is the code of AAAI'21 paper "Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors".
PyTorch implementation of Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations (in AAAI 2021)
Supplementary material and code for "From Label Smoothing to Label Relaxation" as published at AAAI 2021.
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks
The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized.
Official Code for "Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction (AAAI 2021)"
Official Code of AAAI 2021 Paper "Multi-level Distance Regularization for Deep Metric Learning"
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation (AAAI 2021)
[AAAI'21] Code release for "Visual Pivoting for (Unsupervised) Entity Alignment".
code for AAAI21 paper "Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion“
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