BIG-bench Machine Learning
2318 papers with code • 1 benchmarks • 1 datasets
This branch include most common machine learning fundamental algorithms.
Benchmarks
These leaderboards are used to track progress in BIG-bench Machine Learning
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Libraries
Use these libraries to find BIG-bench Machine Learning models and implementations
BIG-bench
Most implemented papers
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
xqx12/daily-info
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These results demonstrate that backdoors in neural networks are both powerful and---because the behavior of neural networks is difficult to explicate---stealthy.
Deep Learning for Classical Japanese Literature
Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks.
GNNExplainer: Generating Explanations for Graph Neural Networks
RexYing/gnn-model-explainer
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NeurIPS 2019
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Geometrically, gender bias is first shown to be captured by a direction in the word embedding.
Consistent Individualized Feature Attribution for Tree Ensembles
A unified approach to explain the output of any machine learning model.
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.
Advances and Open Problems in Federated Learning
FedML-AI/FedML
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FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Adversarial Machine Learning at Scale
Adversarial examples are malicious inputs designed to fool machine learning models.
Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments
dividiti/ck-request-asplos18-mobilenets-armcl-opencl
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Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming.
A high-bias, low-variance introduction to Machine Learning for physicists
The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists.
