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[ICML 2021 Spotlight] DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning | by Elsa Lab | Sep, 2021 | Medium

elsaresearchlab.medium.com | 6 min read | listed 3 hours ago in Machine Learning - Topic feed @ Medium

ICML 2021 Full Paper

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Knowledge is reward: Learning optimal exploration by predictive reward cashing – arXiv Vanity

www.arxiv-vanity.com | 5 min read | listed 8 hours ago in reddit/MachineLearning/[R] Knowledge is reward: Learning optimal exploration by predictive reward cashing

There is a strong link between the general concept of intelligence and the ability to collect and use information. The theory of Bayes-adaptive exploration offers an attractive optimality framework for training machines to perform complex information gathering tasks. However, the computational complexity of the resulting optimal control problem has limited the diffusion of the theory to mainstream deep AI research. In this paper we exploit the inherent mathematical structure of Bayes-adaptive problems in order to dramatically simplify the problem by making the reward structure denser while simultaneously decoupling the learning of exploitatio...

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[2109.08518] Knowledge is reward: Learning optimal exploration by predictive reward cashing

arxiv.org | 1 min read | listed 8 hours ago in reddit/MachineLearning/[R] Knowledge is reward: Learning optimal exploration by predictive reward cashing

There is a strong link between the general concept of intelligence and the ability to collect and use information. The theory of Bayes-adaptive exploration offers an attractive optimality framework for training machines to perform complex information gathering tasks. However, the computational complexity of the resulting optimal control problem has limited the diffusion of the theory to mainstream deep AI research. In this paper we exploit the inherent mathematical structure of Bayes-adaptive problems in order to dramatically simplify the problem by making the reward structure denser while simultaneously decoupling the learning of exploitatio...

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Using PettingZoo with RLlib for Multi-Agent Deep Reinforcement Learning | by J K Terry | Sep, 2021 | Towards Data Science

towardsdatascience.com | 5 min read | listed 9 hours ago in reddit/deeplearning/New tutorial: "Using PettingZoo with RLlib for Multi-Agent Deep Reinforcement Learning"

A tutorial on using PettingZoo multi-agent environments with the RLlib reinforcement learning library

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Best Graph Neural Network architectures: GCN, GAT, MPNN and more | AI Summer

theaisummer.com | 19 min read | listed 11 hours ago in reddit/DeepLearningPapers/Best Graph Neural Network architectures: GCN, GAT, MPNN and more

Explore the most popular gnn architectures such as gcn, gat, mpnn, graphsage and temporal graph networks

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GitHub - cybercore-co-ltd/CoLAD_paper: Improve Object Detection with Label Assignment Distillation.

github.com | 6 min read | listed 11 hours ago in reddit/MachineLearning/[N][D][R] Alleged plagiarism of “Improve Object Detection by Label Assignment Distillation.” (arXiv 2108.10520) by "Label Assignment Distillation for Object Detection" (arXiv 2109.07843). What should I do?

Improve Object Detection with Label Assignment Distillation. - GitHub - cybercore-co-ltd/CoLAD_paper: Improve Object Detection with Label Assignment Distillation.

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[2108.10520] Improving Object Detection by Label Assignment Distillation

arxiv.org | 1 min read | listed 11 hours ago in reddit/MachineLearning/[N][D][R] Alleged plagiarism of “Improve Object Detection by Label Assignment Distillation.” (arXiv 2108.10520) by "Label Assignment Distillation for Object Detection" (arXiv 2109.07843). What should I do?

Label assignment in object detection aims to assign targets, foreground or background, to sampled regions in an image. Unlike labeling for image classification, this problem is not well defined due to the object's bounding box. In this paper, we investigate the problem from a perspective of distillation, hence we call Label Assignment Distillation (LAD). Our initial motivation is very simple, we use a teacher network to generate labels for the student. This can be achieved in two ways: either using the teacher's prediction as the direct targets (soft label), or through the hard labels dynamically assigned by the teacher (LAD). Our experiments...

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[2109.07843] Label Assignment Distillation for Object Detection

arxiv.org | 1 min read | listed 11 hours ago in reddit/MachineLearning/[N][D][R] Alleged plagiarism of “Improve Object Detection by Label Assignment Distillation.” (arXiv 2108.10520) by "Label Assignment Distillation for Object Detection" (arXiv 2109.07843). What should I do?

Knowledge distillation methods are proved to be promising in improving the performance of neural networks and no additional computational expenses are required during the inference time. For the sake of boosting the accuracy of object detection, a great number of knowledge distillation methods have been proposed particularly designed for object detection. However, most of these methods only focus on feature-level distillation and label-level distillation, leaving the label assignment step, a unique and paramount procedure for object detection, by the wayside. In this work, we come up with a simple but effective knowledge distillation approach...

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Deep learning and ECG vs Myocardial infarction | by Knowledgator Engineering | Sep, 2021 | Medium

medium.com | 2 min read | listed 14 hours ago in Artificial Intelligence - Topic feed @ Medium

Myocardial infarction (MI) is a major global health care burden, with an estimated 7.29 million MIs and 110.55 million prevalent cases of…

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Offline RL Workflow

sites.google.com | 2 min read | listed 17 hours ago in Synced Global AI Weekly — Synced Global AI Weekly 2021.9.25

Main Paper + Appendix: Paper Link DR3 used in this paper: DR3 preprint

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[2107.08661] Translatotron 2: Robust direct speech-to-speech translation

arxiv.org | 1 min read | listed 17 hours ago in Synced Global AI Weekly — Synced Global AI Weekly 2021.9.25

We present Translatotron 2, a neural direct speech-to-speech translation model that can be trained end-to-end. Translatotron 2 consists of a speech encoder, a phoneme decoder, a mel-spectrogram synthesizer, and an attention module that connects all the previous three components. Experimental results suggest that Translatotron 2 outperforms the original Translatotron by a large margin in terms of translation quality and predicted speech naturalness, and drastically improves the robustness of the predicted speech by mitigating over-generation, such as babbling or long pause. We also propose a new method for retaining the source speaker's voice ...

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National-scale greenhouse mapping for high spatial resolution remote sensing imagery using a dense object dual-task deep learning framework: A case study of China - ScienceDirect

www.sciencedirect.com | 2 min read | listed 20 hours ago in European Media Monitor - Deep Learning

Greenhouses have revolutionized farming all over the world. To estimate vegetable yields, greenhouse mapping using high spatial resolution (HSR) remot…

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VGPNN: Diverse Generation from a Single Video Made Possible

nivha.github.io | 2 min read | listed 21 hours ago in reddit/ArtificialInteligence/VGPNN: Generate Video Variations - No dataset or deep learning required, Only Nearest Neighbors!
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SuperGlue CVPR 2020

psarlin.com | 2 min read | listed 1 day ago in reddit/MachineLearning/[R] LoFTR: Detector-Free Local Feature Matching with Transformers
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[2104.00680] LoFTR: Detector-Free Local Feature Matching with Transformers

arxiv.org | 1 min read | listed 1 day ago in reddit/MachineLearning/[R] LoFTR: Detector-Free Local Feature Matching with Transformers

We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches at a fine level. In contrast to dense methods that use a cost volume to search correspondences, we use self and cross attention layers in Transformer to obtain feature descriptors that are conditioned on both images. The global receptive field provided by Transformer enables our method to produce dense matches in low-texture areas, where feature detectors usually struggle to produce repeatable...

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[2006.16841] Conditional Set Generation with Transformers

arxiv.org | 1 min read | listed 1 day ago in reddit/MachineLearning/[R] Pix2seq: A Language Modeling Framework for Object Detection. “We simply cast object detection as a language modeling task conditioned on pixels!”

A set is an unordered collection of unique elements--and yet many machine learning models that generate sets impose an implicit or explicit ordering. Since model performance can depend on the choice of order, any particular ordering can lead to sub-optimal results. An alternative solution is to use a permutation-equivariant set generator, which does not specify an order-ing. An example of such a generator is the DeepSet Prediction Network (DSPN). We introduce the Transformer Set Prediction Network (TSPN), a flexible permutation-equivariant model for set prediction based on the transformer, that builds upon and outperforms DSPN in the quality ...

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[1506.04878] End-to-end people detection in crowded scenes

arxiv.org | 1 min read | listed 1 day ago in reddit/MachineLearning/[R] Pix2seq: A Language Modeling Framework for Object Detection. “We simply cast object detection as a language modeling task conditioned on pixels!”

Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. Because we generate predictions jointly, common post-processing steps such as non-maximum suppression are unnecessary. We use a recurrent LSTM layer for sequence generation and train our model end-to-end with a new loss function that operates on sets of detections. We demonstrate the effectiveness of our approach on the challengi...

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Pix2seq: A Language Modeling Framework for Object Detection – arXiv Vanity

www.arxiv-vanity.com | 3 min read | listed 1 day ago in reddit/MachineLearning/[R] Pix2seq: A Language Modeling Framework for Object Detection. “We simply cast object detection as a language modeling task conditioned on pixels!”

This paper presents Pix2Seq, a simple and generic framework for object detection. Unlike existing approaches that explicitly integrate prior knowledge about the task, we simply cast object detection as a language modeling task conditioned on the observed pixel inputs. Object descriptions (e.g., bounding boxes and class labels) are expressed as sequences of discrete tokens, and we train a neural net to perceive the image and generate the desired sequence. Our approach is based mainly on the intuition that if a neural net knows about where and what the objects are, we just need to teach it how to read them out. Beyond the use of task-specific d...

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[2109.10862] Recursively Summarizing Books with Human Feedback

arxiv.org | 1 min read | listed 1 day ago in reddit/MachineLearning/[R] Recursively Summarizing Books with Human Feedback

A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this problem on the task of abstractive summarization of entire fiction novels. Our method combines learning from human feedback with recursive task decomposition: we use models trained on smaller parts of the task to assist humans in giving feedback on the broader task. We collect a large volume of demonstrations and comparisons from human labelers, and fine-tune GPT-3 using behavioral cloning and reward modeling to do summarization recursively. At inference time, the mode...

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NEAT Spiking Neural Networks for Reinforcement Learning | by Dickson Wu | Geek Culture | Sep, 2021 | Medium

medium.com | 1 min read | listed 1 day ago in Machine Learning - Topic feed @ Medium

Paper Summary: “Evolving Spiking Neural Networks for Nonlinear Control Problems”

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Interbreeding between archaic and modern humans - Wikipedia

en.wikipedia.org | 27 min read | listed 1 day ago in reddit/transhumanism/The first time that a non-human with human intelligence is born (eg a rogue scientist clones a neanderthal somewhere inside the EU and raises it there as a normal human,) what rights (if any) will it have at 21 years of age if it has average IQ? What interactions will be legal to engage in with it?
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Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams | Nature Communications

www.nature.com | 16 min read | listed 1 day ago in Twitter — recent in #ai

Ultrasound is an important imaging modality for the detection and characterization of breast cancer, but it has been noted to have high false-positive rates. Here, the authors present an artificial intelligence system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound imaging.

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Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document – arXiv Vanity

www.arxiv-vanity.com | 5 min read | listed 1 day ago in reddit/MachineLearning/Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document

Given the recent proliferation of false claims online, there has been a lot of manual fact-checking effort. As this is very time-consuming, human fact-checkers can benefit from tools that can support them and make them more efficient. Here, we focus on building a system that could provide such support. Given an input document, it aims to detect all sentences that contain a claim that can be verified by some previously fact-checked claims (from a given database). The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence. Unlike previous work...

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[2109.07410] Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document

arxiv.org | 1 min read | listed 1 day ago in reddit/MachineLearning/Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document

Given the recent proliferation of false claims online, there has been a lot of manual fact-checking effort. As this is very time-consuming, human fact-checkers can benefit from tools that can support them and make them more efficient. Here, we focus on building a system that could provide such support. Given an input document, it aims to detect all sentences that contain a claim that can be verified by some previously fact-checked claims (from a given database). The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence. Unlike previous work...

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Deep learning for early warning signals of tipping points | PNAS

www.pnas.org | 2 min read | listed 1 day ago in European Media Monitor - Artificial Intelligence

Early warning signals (EWS) of tipping points are vital to anticipate system collapse or other sudden shifts. However, existing generic early warning indicators designed to work across all systems do not provide information on the state that lies beyond the tipping point. Our results show how deep learning algorithms (artificial intelligence) can provide EWS of tipping points in real-world systems. The algorithm predicts certain qualitative aspects of the new state, and is also more sensitive and generates fewer false positives than generic indicators. We use theory about system behavior near tipping points so that the algorithm does not requ...

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