68 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

Gradient Harmonized Single-stage Detector

libuyu/GHM_Detection 13 Nov 2018

Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i. e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples.

Pylearn2: a machine learning research library

lisa-lab/pylearn2 20 Aug 2013

Pylearn2 is a machine learning research library.

MIOpen: An Open Source Library For Deep Learning Primitives

ROCmSoftwarePlatform/MIOpen 30 Sep 2019

Deep Learning has established itself to be a common occurrence in the business lexicon.

Neural Network Distiller: A Python Package For DNN Compression Research

NervanaSystems/distiller 27 Oct 2019

This paper presents the philosophy, design and feature-set of Neural Network Distiller, an open-source Python package for DNN compression research.

ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense

softsys4ai/athena 2 Jan 2020

There has been extensive research on developing defense techniques against adversarial attacks; however, they have been mainly designed for specific model families or application domains, therefore, they cannot be easily extended.

Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks

ZhaofanQiu/pseudo-3d-residual-networks ICCV 2017

In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating $3\times3\times3$ convolutions with $1\times3\times3$ convolutional filters on spatial domain (equivalent to 2D CNN) plus $3\times1\times1$ convolutions to construct temporal connections on adjacent feature maps in time.

AXNet: ApproXimate computing using an end-to-end trainable neural network

PengZhenghao/AXNet 27 Jul 2018

To guarantee the approximation quality, existing works deploy two neural networks (NNs), e. g., an approximator and a predictor.

Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains

yoortwijn/quine-ground-truth COLING 2020

We present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings.

How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

zhijing-jin/NLP4SocialGood_Papers Findings (ACL) 2021

We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research.