Trending Research
Semantic Image Synthesis with Spatially-Adaptive Normalization
We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout.
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
We compare Dragonfly to a suite of other packages and algorithms for global optimisation and demonstrate that when the above methods are integrated, they enable significant improvements in the performance of BO.
Multi-Task Deep Neural Networks for Natural Language Understanding
• namisan/mt-dnn
• 
In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks.
SOTA for Natural Language Inference on SNLI
DOMAIN ADAPTATION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SENTIMENT ANALYSIS
Selective Kernel Networks
A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches.
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
Bags of Tricks and A Strong Baseline for Deep Person Re-identification
• michuanhaohao/reid-strong-baseline
In the literature, some effective training tricks are briefly appeared in several papers or source codes.
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
• Daniil-Osokin/lightweight-human-pose-estimation.pytorch
•
In this work we adapt multi-person pose estimation architecture to use it on edge devices.
Attention Is All You Need
NeurIPS 2017
• audier/DeepSpeechRecognition
• 
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Splitter: Learning Node Representations that Capture Multiple Social Contexts
WWW 2019
• benedekrozemberczki/Splitter
•
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
SOTA for Common Sense Reasoning on SWAG
COMMON SENSE REASONING CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION QUESTION ANSWERING