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Zipf's law - Wikipedia

en.wikipedia.org | 13 min read | listed 16 hours ago in reddit/LanguageTechnology/How to test statistical significance on text data?
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Meditation Programs for Psychological Stress and Well-being: A Systematic Review and Meta-analysis | Complementary and Alternative Medicine | JAMA Internal Medicine | JAMA Network

jamanetwork.com | 20 min read | listed 4 days ago in reddit/transhumanism/Anti Aging and Discovery
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GitHub - taki0112/CIPS-Tensorflow: Simple Tensorflow implementation of "Image Generators with Conditionally-Independent Pixel Synthesis" (CVPR 2021 Oral)

github.com | 1 min read | listed 3 days ago in reddit/MachineLearning/[P] Simple Tensorflow implementation of "Image Generators with Conditionally-Independent Pixel Synthesis (CVPR 2021, Oral)"

Simple Tensorflow implementation of "Image Generators with Conditionally-Independent Pixel Synthesis" (CVPR 2021 Oral) - GitHub - taki0112/CIPS-Tensorflow: Simple Tensorflow implementatio...

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AI in health and medicine | Nature Medicine

www.nature.com | 10 min read | listed 4 days ago in Artificial Intelligence

AI has the potential to reshape medicine and make healthcare more accurate, efficient and accessible; this Review discusses recent progress, opportunities and challenges toward achieving this goal.

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[1703.10651] Reliable Decision Support using Counterfactual Models

arxiv.org | 1 min read | listed 6 days ago in reddit/MachineLearning/[P] How do we account for ‘confounding due to indication’ while training a model?

Decision-makers are faced with the challenge of estimating what is likely to happen when they take an action. For instance, if I choose not to treat this patient, are they likely to die? Practitioners commonly use supervised learning algorithms to fit predictive models that help decision-makers reason about likely future outcomes, but we show that this approach is unreliable, and sometimes even dangerous. The key issue is that supervised learning algorithms are highly sensitive to the policy used to choose actions in the training data, which causes the model to capture relationships that do not generalize. We propose using a different learnin...

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[1603.04259] Item2Vec: Neural Item Embedding for Collaborative Filtering

arxiv.org | 1 min read | listed 4 days ago in reddit/MachineLearning/Item2Vec - Word2Vec from gensim wrapped as sklearn estimator for GridSearchCV [P]

Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that p...

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LSTM — PyTorch 1.9.1 documentation

pytorch.org | 8 min read | listed 5 days ago in reddit/deeplearning/PyTorch LSTM Output Confusion
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DeepSpeed: Advancing MoE inference and training to power next-generation AI scale - Microsoft Research

www.microsoft.com | 26 min read | listed 5 days ago in Microsoft Research Blog

In the last three years, the largest trained dense models have increased in size by over 1,000 times, from a few hundred million parameters to over 500 billion parameters in Megatron-Turing NLG 530B (MT-NLG). Improvements in model quality with size suggest that this trend will continue, with larger model sizes bringing better model quality. However, […]

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Noether Networks: meta-learning useful conserved quantities | noether-networks

dylandoblar.github.io | 1 min read | listed 5 days ago in reddit/MachineLearning/[D] First Author Interview - Noether Networks: Meta-Learning Useful Conserved Quantities (Paper Explained Video & Interview)

Meta-learning inductive biases in the form of useful conserved quantities.

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Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning | Nature Communications

www.nature.com | 12 min read | listed 4 days ago in European Media Monitor - Deep Learning

Deep learning unveils a nonlinear sensitivity of glacier mass changes to future climate warming, with important implications for water resources and sea-level rise coming from glaciers and particularly ice caps.

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Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

wenlong.page | 1 min read | listed 2 days ago in Synced Global AI Weekly — Synced Global AI Weekly 2022.1.22
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[2109.08958] AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks

arxiv.org | 1 min read | listed 5 days ago in reddit/MachineLearning/[R] AutoInit Software for Model Initialization

Neural networks require careful weight initialization to prevent signals from exploding or vanishing. Existing initialization schemes solve this problem in specific cases by assuming that the network has a certain activation function or topology. It is difficult to derive such weight initialization strategies, and modern architectures therefore often use these same initialization schemes even though their assumptions do not hold. This paper introduces AutoInit, a weight initialization algorithm that automatically adapts to different neural network architectures. By analytically tracking the mean and variance of signals as they propagate throu...

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Education LIbrary Psychological Harm - Next Level Intactivism

www.nextlevelintactivism.com | 25 min read | listed 4 days ago in reddit/MachineLearning/[D] Autonomous weapons are here and the world is divided over their use

EDUCATION LIBRARY PSYCHOLOGICAL HARM “The worst circumcision scar is the one left in the brain of its victims.” ACE Adverse Childhood Experiences Affect the Brain for Life The agony and… Read More »Education LIbrary Psychological Harm

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[1606.05830] Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

arxiv.org | 1 min read | listed 3 days ago in reddit/robotics/Advances in SLAM since 2016

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for m...

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[2201.08239] LaMDA: Language Models for Dialog Applications

arxiv.org | 2 min read | listed 11 hours ago in Import AI 281: China does more surveillance research than US and Europe; Google reveals its text model LaMDA; Microsoft improves MoEs

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are co...

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Worldline numerics applied to custom Casimir geometry generates unanticipated intersection with Alcubierre warp metric | SpringerLink

link.springer.com | 9 min read | listed 1 day ago in reddit/transhumanism/What is the next big progression after Transhumanism?

While conducting analysis related to a DARPA-funded project to evaluate possible structure of the energy density present in a Casimir cavity as predicted by the dynamic vacuum model, a micro/nano-scale structure has been discovered that predicts negative energy density distribution that closely matches requirements for the Alcubierre metric. The simplest notional geometry being analyzed as part of the DARPA-funded work consists of a standard parallel plate Casimir cavity equipped with pillars arrayed along the cavity mid-plane with the purpose of detecting a transient electric field arising from vacuum polarization conjectured to occur along ...

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[1810.09050] A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling

arxiv.org | 1 min read | listed 9 hours ago in reddit/tensorflow/Attention pooling layer in Keras

Sound event detection (SED) entails two subtasks: recognizing what types of sound events are present in an audio stream (audio tagging), and pinpointing their onset and offset times (localization). In the popular multiple instance learning (MIL) framework for SED with weak labeling, an important component is the pooling function. This paper compares five types of pooling functions both theoretically and experimentally, with special focus on their performance of localization. Although the attention pooling function is currently receiving the most attention, we find the linear softmax pooling function to perform the best among the five. Using t...

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Beyond LDA: State-of-the-art Topic Models With BigARTM - Topic Modeling for Text with BigARTM

databricks.com | 11 min read | listed 3 days ago in Databricks

Learn more about the open-source text modeling project BigARTM and how it surpasses other techniques, such as LDA, for NLP and other semantic use cases.

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Twin-field quantum key distribution (QKD) across an 830-km fibre

phys.org | 8 min read | listed 2 hours ago in reddit/singularity/Twin field quantum key distribution (QKD) across an 830-km fibre

By using quantum key distribution (QKD), quantum cryptographers can share information via theoretic secure keys between remote peers through physics-based protocols. The laws of quantum physics dictate that photons carrying signals cannot be amplified or relayed through classical optical methods to maintain quantum security. The resulting transmission loss of the channel can limit its achievable distance to form a huge barrier to build large-scale quantum secure networks. In a new report now published in Nature Photonics, Shuang Wang and a research team in quantum information, cryptology and quantum physics in China developed an experimental ...

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[2012.09807] BERT Goes Shopping: Comparing Distributional Models for Product Representations

arxiv.org | 1 min read | listed 4 days ago in reddit/LanguageTechnology/Item2Vec - Word2Vec from gensim wrapped as sklearn estimator for GridSearchCV

Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}. Inspired by the recent performance improvements on several NLP tasks brought by contextualized embeddings, we propose to transfer BERT-like architectures to eCommerce: our model -- ~\textit{Prod2BERT} -- is trained to generate representations of products through masked session modeling. Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of~\textit{Prod2BERT} and~\textit{prod2vec} embeddings: while~\textit{Prod2BERT} is found to be superior in sev...

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[2201.05610] When less is more: Simplifying inputs aids neural network understanding

arxiv.org | 1 min read | listed 3 days ago in DataScienceWeekly.org — Data Science Weekly - Issue 426

How do neural network image classifiers respond to simpler and simpler inputs? And what do such responses reveal about the learning process? To answer these questions, we need a clear measure of input simplicity (or inversely, complexity), an optimization objective that correlates with simplification, and a framework to incorporate such objective into training and inference. Lastly we need a variety of testbeds to experiment and evaluate the impact of such simplification on learning. In this work, we measure simplicity with the encoding bit size given by a pretrained generative model, and minimize the bit size to simplify inputs in training a...

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Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis - PubMed

pubmed.ncbi.nlm.nih.gov | 1 min read | listed 3 days ago in European Media Monitor - Machine Learning

Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics …

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[2201.05242] Neural Circuit Architectural Priors for Embodied Control

arxiv.org | 2 min read | listed 4 days ago in reddit/MachineLearning/[R] Neural Circuit Architectural Priors for Embodied Control

Artificial neural networks for simulated motor control and robotics often adopt generic architectures like fully connected MLPs. While general, these tabula rasa architectures rely on large amounts of experience to learn, are not easily transferable to new bodies, and have internal dynamics that are difficult to interpret. In nature, animals are born with highly structured connectivity in their nervous systems shaped by evolution; this innate circuitry acts synergistically with learning mechanisms to provide inductive biases that enable most animals to function well soon after birth and improve abilities efficiently. Convolutional networks in...

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Learning in continuous action space for developing high dimensional potential energy models | Nature Communications

www.nature.com | 11 min read | listed 6 days ago in European Media Monitor - Deep Learning

Reinforcement learning algorithms are emerging as powerful machine learning approaches. This paper introduces a novel machine-learning approach for learning in continuous action space and applies this strategy to the generation of high dimensional potential models for a wide variety of materials.

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[2201.07372] Prospective Learning: Back to the Future

arxiv.org | 2 min read | listed 3 days ago in DataScienceWeekly.org — Data Science Weekly - Issue 426

Research on both natural intelligence (NI) and artificial intelligence (AI) generally assumes that the future resembles the past: intelligent agents or systems (what we call 'intelligence') observe and act on the world, then use this experience to act on future experiences of the same kind. We call this 'retrospective learning'. For example, an intelligence may see a set of pictures of objects, along with their names, and learn to name them. A retrospective learning intelligence would merely be able to name more pictures of the same objects. We argue that this is not what true intelligence is about. In many real world problems, both NIs and A...

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