Run CAPE
python train_causal.py --loop 10 -m cape_cau -d NI --i_t 1
Run a baseline model
python train_causal_baselines.py --loop 10 -m cfrmmd -d NI --i_t 1
Run CAPE
python train_event_with_causal.py --loop 10 -m cape -d NI
Add noise to data
python train_event_with_causal.py --loop 10 -m cape -d NI --train_noise 0.1
3d-ken-burns This is a reference implementation of 3D Ken Burns Effect from a Single Image [1] using PyTorch. Given a single input image, it animates
Official repository of the paper (UAI 2021) "A Variational Approximation for Analyzing the Dynamics of Panel Data", Mixed Effect Neural ODE. Panel dat
resco Description resco is a simple python package that report the effect of dee
Effect of Different Encodings and Distance Functions on Quantum Instance-based Classifiers The repository contains the code to reproduce the experimen
merged_depth runs (1) AdaBins, (2) DiverseDepth, (3) MiDaS, (4) SGDepth, and (5) Monodepth2, and calculates a weighted-average per-pixel absolute dept
OpenGaze: Web Service for OpenFace Facial Behaviour Analysis Toolkit Overview OpenFace is a fantastic tool intended for computer vision and machine le
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light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F
Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia
This repository doesn't contain the code to process the input data into the pickle files in the data folder. The paper says this:
Events are categorized into 20 main categories (e.g., appeal, demand, protest, etc.) according to CAMEO methodology [59 ]. Each event is encoded with geolocation, time (day, month, year), category, etc.
Is it possible to release the code to do the above or at least provide a high-level description of the process in the README? I'm especially interested in how you handled the GDELT data.
CausalNLP CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable. Install pip install -U
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au
We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time.
Music Source Separation Music source separation is a task to separate audio recordings into individual sources. This repository is an PyTorch implmeme
Hello from magnus Magnus provides four capabilities for data teams: Compute execution plan: A DAG representation of work that you want to get done. In
Audio Source Separation is the process of separating a mixture into isolated sounds from individual sources (e.g. just the lead vocals).
DRNet for Video Indvidual Counting (CVPR 2022) Introduction This is the official PyTorch implementation of paper: DR.VIC: Decomposition and Reasoning
micro-TCN Efficient neural networks for audio effect modeling
Counterfactual VQA (CF-VQA) This repository is the Pytorch implementation of our paper "Counterfactual VQA: A Cause-Effect Look at Language Bias" in C
PyEffect: Algebraic effects in Python What IDK. Usage effects.handle(operation, handlers=None) effects.set_handler(effect, handler) Supported effects
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