Papers with code8/14/2023 DeepFaceLab provides an easy-to-use pipeline for people with no comprehensive understanding of deep learning framework or model implementation, while remains a flexible and loose coupling structure for people who need to strengthen their own pipeline with other features without writing complicated code. It is an open-source deepfake system created by iperov for face swapping with more than 3K forks and 13,000 stars in GitHub. DeepFaceLab: A simple, flexible and extensible face-swapping framework It is computation and memory efficient, and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. ![]() In addition, it offers auto differentiation to derive gradients. Embedded in the host language, it blends ‘declarative symbolic expression’ with imperative tensor computation. MXNet is a multi-language ML library to ease the development of ML algorithms, especially for deep neural networks (DNNs). MXNet: A Flexible & Efficient Machine Learning Library for ‘Heterogeneous Distributed Systems AutoML is expected to go further, where it can automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. However, this progress has focused mainly on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks. AutoML-Zero: Evolving Machine Learning Algorithms From ScratchĪutoML has made significant progress in recent times. The source code and documentation are available on SciKit. It focuses on bringing machine learning to non-specialists using a general-purpose, high-level language. ![]() ![]() Scikit-learn is a Python module integrating a wide range of SOTA machine learning algorithms for medium-scale ‘supervised’ and ‘unsupervised’ problems. It is the process of explicitly training a model on adversarial examples to make it more robust to attack or reduce its test error on clean inputs. It transfers from one model to another, allowing attackers to mount black box attacks without knowing the target model’s parameters. Adversarial Machine Learning at ScaleĪdversarial examples are malicious inputs designed to fool machine learning models. The machine learning system maps the nodes of a dataflow graph across many machines in a cluster and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs TPUs. It uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. TensorFlow is an ML system that operates at a large scale and in heterogeneous environments. Here, we have rounded up the top 10 machine learning research papers on ‘Papers With Code.’ TensorFlow: A system for large-scale machine learning Its open-source, community-centric approach offers researchers access to papers, frameworks, datasets, libraries, models, benchmarks, etc. Papers With Code is a self-contained team within Facebook AI Research. The platform consists of 4,995 benchmarks, 2,305 tasks, and 49,190 papers with code.īesides Papers With Code, other notable machine learning research papers’ resources and tools include arXiv Sanity, 42 Papers, Crossminds, Connected Papers etc. Papers With Code is the go-to resource for the latest SOTA ML papers, code, results for discovery and comparison.
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