Philosophy
90 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Philosophy
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BIG-bench
Most implemented papers
Gradient Harmonized Single-stage Detector
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
Pylearn2 is a machine learning research library.
MIOpen: An Open Source Library For Deep Learning Primitives
Deep Learning has established itself to be a common occurrence in the business lexicon.
Neural Network Distiller: A Python Package For DNN Compression Research
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
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.
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
In this paper, we comprehensively study three architecture design choices on ViT -- spatial reduction, doubled channels, and multiscale features -- and demonstrate that a vanilla ViT architecture can fulfill this goal without handcrafting multiscale features, maintaining the original ViT design philosophy.
VanillaNet: the Power of Minimalism in Deep Learning
In this study, we introduce VanillaNet, a neural network architecture that embraces elegance in design.
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
WenjieDu/PyPOTS
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PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i. e. incomplete time series with missing values, A. K. A.
Genetic Algorithm for Solving Simple Mathematical Equality Problem
This paper explains genetic algorithm for novice in this field.
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
ZhaofanQiu/pseudo-3d-residual-networks
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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.
