Papers With Code is the go-to resource for the newest SOTA ML papers, code, outcomes for discovery and comparability. The platform consists of 4,995 benchmarks, 2,305 duties, and 49,190 papers with code.
Papers With Code is a self-contained group inside Fb AI Analysis. Its open-source, community-centric method affords researchers entry to papers, frameworks, datasets, libraries, fashions, benchmarks, and many others.
Right here, now we have rounded up the top 10 machine learning research papers on ‘Papers With Code.’
TensorFlow is an ML system that operates at a big scale and in heterogeneous environments. It makes use of dataflow graphs to signify computation, shared state, and the operations that mutate that state. The machine studying system maps the nodes of a dataflow graph throughout many machines in a cluster and inside a machine throughout a number of computational units, together with multicore CPUs, general-purpose GPUs, and custom-designed ASICs TPUs. The code is on the market on GitHub.
Adversarial examples are malicious inputs designed to idiot machine studying fashions. It transfers from one mannequin to a different, permitting attackers to mount black field assaults with out understanding the goal mannequin’s parameters. It’s the strategy of explicitly coaching a mannequin on adversarial examples to make it extra strong to assault or cut back its take a look at error on clear inputs.
Scikit-learn is a Python module integrating a variety of SOTA machine studying algorithms for medium-scale ‘supervised’ and ‘unsupervised’ issues. It focuses on bringing machine studying to non-specialists utilizing a general-purpose, high-level language. The supply code and documentation can be found on SciKit.
AutoML has made vital progress in current occasions. Nonetheless, this progress has centered primarily on the structure of neural networks, the place it has relied on subtle expert-designed layers as constructing blocks. AutoML is predicted to go additional, the place it might routinely uncover full machine studying algorithms simply utilizing fundamental mathematical operations as constructing blocks.
MXNet is a multi-language ML library to ease the event of ML algorithms, particularly for deep neural networks (DNNs). Embedded within the host language, it blends ‘declarative symbolic expression’ with crucial tensor computation. As well as, it affords auto differentiation to derive gradients. It’s computation and reminiscence environment friendly, and runs on varied heterogeneous methods, starting from cell units to distributed GPU clusters.
It’s an open-source deepfake system created by iperov for face swapping with greater than 3K forks and 13,000 stars in GitHub. DeepFaceLab gives an easy-to-use pipeline for folks with no complete understanding of deep studying framework or mannequin implementation, whereas stays a versatile and free coupling construction for individuals who have to strengthen their very own pipeline with different options with out writing difficult code. Greater than 95% of deepfake movies are created with DeepFaceLab. The code is on the market on GitHub.
On this paper, you possibly can convert non-polite sentences to well mannered sentences whereas preserving the which means. It gives a dataset of greater than 1.39 cases routinely labeled for politeness to encourage benchmark evaluations on this new job. For politeness and 5 different switch duties, its mannequin outperforms the SOTA strategies on automated metrics for content material preservation, with a comparable or higher efficiency on model switch accuracy. Moreover, the mannequin surpasses current strategies on human evaluations for grammaticality, which means preservation and switch accuracy throughout all of the six model switch duties. The info and code can be found on GitHub.
Caffe gives researchers with a clear and modifiable framework for SOTA deep studying algorithms and a set of reference fashions. The framework is a BSD-licensed C++ library with MATLAB and Python bindings for coaching and deploying general-purpose CNNs and different deep fashions effectively on commodity architectures. The supply code is on the market on GitHub.
The paper exhibits pre-training is essential to smaller architectures, and fine-tuning pre-trained compact fashions might be aggressive to extra elaborate strategies proposed in concurrent work. The paper explores pre-trained fashions and transferring job data from massive fine-tuned fashions via normal data distillation. Because of this, the final algorithm, together with pre-trained distillation, brings enhancements.
The paper describes a scalable end-to-end tree boosting system known as XGBoost, used broadly by knowledge scientists to attain SOTA outcomes on many machine studying challenges. The supply code is on the market on GitHub.
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