shubhampachori12110095 Goto Github PK

conditional-image-generation

conditional-neural-process

conditional-pixelcnn-decoder

Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network

cone-greedy

confident-sinkhorn-allocation

Pseudo-labeling for tabular data

confident_classifier

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

conformer-rlpatching

Conformer-RLpatching achieves multi-objective dispatching for the hybrid power system under the long-term fluctuations of renewable energy

confusion

Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"

connect4-alpha-zero

Connect4 reinforcement learning by AlphaGo Zero methods.

conspeech

Political Speech Generator

constituent-centric-neural-architecture-for-reading-comprehension

Tensorflow implementation of Constituent-Centric Neural Architecture for Reading Comprehension

constrained-graph-variational-autoencoder

Sample code for Constrained Graph Variational Autoencoders

contentextraction

Content Extraction via Text Density (SIGIR11)

context-aware-click-model

A Context-Aware Click Model for Web Search. WSDM 2020

context-aware-multi-modal-transportation-recommendation

KDD CUP 2019 Baseline

context-encoder

[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

context-selection-embedding

Context Selection for Embedding Models

context2vec

PyTorch implementation of context2vec from Melamud et al., CoNLL 2016

context_encoder_pytorch

PyTorch Implement of Context Encoders: Feature Learning by Inpainting

contextlocnet

ContextLocNet: Context-aware Deep Network Models for Weakly Supervised Localization (ECCV 2016)

contextual-utterance-level-multimodal-sentiment-analysis

Context-Dependent Sentiment Analysis in User-Generated Videos

contiguous-succotash

Recurrent Variational Autoencoder with Dilated Convolutions that generates sequential data implemented in pytorch

contrastivelosses4vrd

Implementation for the CVPR2019 paper "Graphical Contrastive Losses for Scene Graph Generation"

contrastivemixup

contrib

Implementations of ideas from recent papers

controlled-text-generation

Reproducing Hu, et. al., ICML 2017's "Toward Controlled Generation of Text"

conv-emotion

This repo contains implementation of different architectures for emotion recognition in conversations

convai-bot-1337

Skill-based Conversational Agent for NIPS Conversational Intelligence Challenge 2017

convcap

convdiclearntensorfactor

Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning. Model parameters are estimated via CP decomposition of the observed higher order input moments. However, in many domains, additional invariances such as shift invariances exist, enforced via models such as convolutional dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter estimation of convolutional models. Our algorithm is based on the popular alternating least squares method, but with efficient projections onto the space of stacked circulant matrices. Our method is embarrassingly parallel and consists of simple operations such as fast Fourier transforms and matrix multiplications. Our algorithm converges to the dictionary much faster and more accurately compared to the alternating minimization over filters and activation maps.