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Tag: deep

  • Take a Deep Dive into Filtering in DAX

    Take a Deep Dive into Filtering in DAX Have you ever wondered what happens when you apply a filter in a DAX expression? Well, Today I will take you on a deep dive into this fascinating topic, with examples to help you learn something new and surprising. The post Take a Deep Dive into Filtering…

  • Optimizing Deep Learning Models with SAM

    Optimizing Deep Learning Models with SAM A deep dive into the Sharpness-Aware-Minimization (SAM) algorithm and how it improves the generalizability of modern deep learning models The post Optimizing Deep Learning Models with SAM appeared first on Towards Data Science. Anindya Dey Go to original source

  • Agentic AI for Modern Deep Learning Experimentation

    Agentic AI for Modern Deep Learning Experimentation Stop babysitting training runs. Start shipping research. Autonomous experiment management built for/by deep learning engineers. The post Agentic AI for Modern Deep Learning Experimentation appeared first on Towards Data Science. Sam Black Go to original source

  • Deep networks learn to parse uniform-depth context-free languages from local statistics

    Deep networks learn to parse uniform-depth context-free languages from local statistics arXiv:2602.06065v1 Announce Type: new Abstract: Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning. Studies of the internal representations of Large Language Models (LLMs) support their ability to parse text…

  • Deep Neural Networks as Iterated Function Systems and a Generalization Bound

    Deep Neural Networks as Iterated Function Systems and a Generalization Bound arXiv:2601.19958v1 Announce Type: new Abstract: Deep neural networks (DNNs) achieve remarkable performance on a wide range of tasks, yet their mathematical analysis remains fragmented: stability and generalization are typically studied in disparate frameworks and on a case-by-case basis. Architecturally, DNNs rely on the recursive…

  • Stochastic Deep Learning: A Probabilistic Framework for Modeling Uncertainty in Structured Temporal Data

    Stochastic Deep Learning: A Probabilistic Framework for Modeling Uncertainty in Structured Temporal Data arXiv:2601.05227v1 Announce Type: new Abstract: I propose a novel framework that integrates stochastic differential equations (SDEs) with deep generative models to improve uncertainty quantification in machine learning applications involving structured and temporal data. This approach, termed Stochastic Latent Differential Inference (SLDI), embeds…

  • Deep Reinforcement Learning: The Actor-Critic Method

    Deep Reinforcement Learning: The Actor-Critic Method Robot friends collaborate to learn to fly a drone The post Deep Reinforcement Learning: The Actor-Critic Method appeared first on Towards Data Science. Vedant Jumle Go to original source

  • Towards a pretrained deep learning estimator of the Linfoot informational correlation

    Towards a pretrained deep learning estimator of the Linfoot informational correlation arXiv:2512.12358v1 Announce Type: new Abstract: We develop a supervised deep-learning approach to estimate mutual information between two continuous random variables. As labels, we use the Linfoot informational correlation, a transformation of mutual information that has many important properties. Our method is based on ground…

  • Gemini Deep Research: Autonomous Intelligence for Enterprise Research

    Gemini Deep Research: Autonomous Intelligence for Enterprise Research submitted by /u/WarChampion90 [link] [comments] /u/WarChampion90 Go to original source

  • Decentralized Computation: The Hidden Principle Behind Deep Learning

    Decentralized Computation: The Hidden Principle Behind Deep Learning Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep-learning-based AI…

  • Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond

    Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond arXiv:2512.04696v1 Announce Type: new Abstract: We develop a flexible feature selection framework based on deep neural networks that approximately controls the false discovery rate (FDR), a measure of Type-I error. The method applies to architectures whose first layer is fully connected. From the second…

  • The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint

    The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint Opening the black box of ML models, step by step, directly in Excel The post The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint appeared first on Towards Data Science. angela shi Go to original source

  • Gradient flow for deep equilibrium single-index models

    Gradient flow for deep equilibrium single-index models arXiv:2511.16976v1 Announce Type: cross Abstract: Deep equilibrium models (DEQs) have recently emerged as a powerful paradigm for training infinitely deep weight-tied neural networks that achieve state of the art performance across many modern machine learning tasks. Despite their practical success, theoretically understanding the gradient descent dynamics for training…

  • How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition

    How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition Introduction Automatic plant leaf detection is a remarkable innovation in computer vision and machine learning, enabling the identification of plant species by examining a photograph of the leaves. Deep learning is applied to extract meaningful features from an image of leaves and convert…

  • VIKING: Deep variational inference with stochastic projections

    VIKING: Deep variational inference with stochastic projections arXiv:2510.23684v1 Announce Type: new Abstract: Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality predictions and uncertainties, the practical reality has been the opposite, with unstable training, poor predictive power, and subpar calibration. Building upon…

  • Deep Reinforcement Learning: 0 to 100

    Deep Reinforcement Learning: 0 to 100 Using RL to teach robots to fly a drone The post Deep Reinforcement Learning: 0 to 100 appeared first on Towards Data Science. Vedant Jumle Go to original source

  • How to Build a Powerful Deep Research System

    How to Build a Powerful Deep Research System Learn how to access vasts amounts of information with your own deep research system The post How to Build a Powerful Deep Research System appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues

    A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues Key lessons I’ve learned running RabbitMQ + Celery in production The post A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues appeared first on Towards Data Science. Clara Chong Go to original source

  • Deep Neural Network-Driven Adaptive Filtering

    Deep Neural Network-Driven Adaptive Filtering arXiv:2508.04258v1 Announce Type: new Abstract: This paper proposes a deep neural network (DNN)-driven framework to address the longstanding generalization challenge in adaptive filtering (AF). In contrast to traditional AF frameworks that emphasize explicit cost function design, the proposed framework shifts the paradigm toward direct gradient acquisition. The DNN, functioning as…

  • From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions

    From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions arXiv:2507.21429v1 Announce Type: new Abstract: The convergence of gradient descent (GD) on the non-convex loss landscapes of deep neural networks (DNNs) presents a fundamental theoretical challenge. While recent work has established that GD converges to a stationary point at a sublinear rate…

  • Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights

    Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights A detailed guide on controlling overfitting and increasing the stability of your models. The post Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights appeared first on Towards Data Science. Sourav Mohile Go to original source

  • You can now automate deep dives, with clear actionable recommendations based on data.

    You can now automate deep dives, with clear actionable recommendations based on data. submitted by /u/phicreative1997 [link] [comments] /u/phicreative1997 Go to original source

  • Convergence of Adam in Deep ReLU Networks via Directional Complexity and Kakeya Bounds

    Convergence of Adam in Deep ReLU Networks via Directional Complexity and Kakeya Bounds arXiv:2505.15013v1 Announce Type: new Abstract: First-order adaptive optimization methods like Adam are the default choices for training modern deep neural networks. Despite their empirical success, the theoretical understanding of these methods in non-smooth settings, particularly in Deep ReLU networks, remains limited. ReLU…

  • Learning Guarantee of Reward Modeling Using Deep Neural Networks

    Learning Guarantee of Reward Modeling Using Deep Neural Networks arXiv:2505.06601v1 Announce Type: new Abstract: In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks. We establish a novel non-asymptotic regret bound for deep reward estimators in a non-parametric setting, which depends explicitly on the network architecture.…

  • On the expressivity of deep Heaviside networks

    On the expressivity of deep Heaviside networks arXiv:2505.00110v1 Announce Type: new Abstract: We show that deep Heaviside networks (DHNs) have limited expressiveness but that this can be overcome by including either skip connections or neurons with linear activation. We provide lower and upper bounds for the Vapnik-Chervonenkis (VC) dimensions and approximation rates of these network…

  • A comparison of generative deep learning methods for multivariate angular simulation

    A comparison of generative deep learning methods for multivariate angular simulation arXiv:2504.21505v1 Announce Type: new Abstract: With the recent development of new geometric and angular-radial frameworks for multivariate extremes, reliably simulating from angular variables in moderate-to-high dimensions is of increasing importance. Empirical approaches have the benefit of simplicity, and work reasonably well in low dimensions,…

  • Evaluating Uncertainty in Deep Gaussian Processes

    Evaluating Uncertainty in Deep Gaussian Processes arXiv:2504.17719v1 Announce Type: new Abstract: Reliable uncertainty estimates are crucial in modern machine learning. Deep Gaussian Processes (DGPs) and Deep Sigma Point Processes (DSPPs) extend GPs hierarchically, offering promising methods for uncertainty quantification grounded in Bayesian principles. However, their empirical calibration and robustness under distribution shift relative to baselines…

  • Towards Interpretable Deep Generative Models via Causal Representation Learning

    Towards Interpretable Deep Generative Models via Causal Representation Learning arXiv:2504.11609v1 Announce Type: new Abstract: Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods’ surprising performance is due in part to their ability to learn implicit “representations”…

  • Deep spatio-temporal point processes: Advances and new directions

    Deep spatio-temporal point processes: Advances and new directions arXiv:2504.06364v1 Announce Type: new Abstract: Spatio-temporal point processes (STPPs) model discrete events distributed in time and space, with important applications in areas such as criminology, seismology, epidemiology, and social networks. Traditional models often rely on parametric kernels, limiting their ability to capture heterogeneous, nonstationary dynamics. Recent innovations…

  • Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks

    Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks arXiv:2504.06470v1 Announce Type: new Abstract: Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep…

  • CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning

    CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning arXiv:2503.18980v1 Announce Type: new Abstract: Exploration remains a critical challenge in reinforcement learning, as many existing methods either lack theoretical guarantees or fall short of practical effectiveness. In this paper, we introduce CAE, a lightweight algorithm that repurposes the value networks in standard deep…

  • Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters

    Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters arXiv:2503.12266v1 Announce Type: new Abstract: We analyze the prior that a Deep Gaussian Process with polynomial kernels induces. We observe that, even for relatively small depths, averaging effects occur within such a Deep Gaussian Process and that the prior can…

  • Deep Research by OpenAI: A Practical Test of AI-Powered Literature Review

    Deep Research by OpenAI: A Practical Test of AI-Powered Literature Review “Conduct a comprehensive literature review on the state-of-the-art in Machine Learning and energy consumption. […]” With this prompt, I tested the new Deep Research function, which has been integrated into the OpenAI o3 reasoning model since the end of February — and conducted a state-of-the-art literature…

  • Learning Dynamics of Deep Linear Networks Beyond the Edge of Stability

    Learning Dynamics of Deep Linear Networks Beyond the Edge of Stability arXiv:2502.20531v1 Announce Type: new Abstract: Deep neural networks trained using gradient descent with a fixed learning rate $eta$ often operate in the regime of “edge of stability” (EOS), where the largest eigenvalue of the Hessian equilibrates about the stability threshold $2/eta$. In this work,…

  • Applications of Statistical Field Theory in Deep Learning

    Applications of Statistical Field Theory in Deep Learning arXiv:2502.18553v1 Announce Type: new Abstract: Deep learning algorithms have made incredible strides in the past decade yet due to the complexity of these algorithms, the science of deep learning remains in its early stages. Being an experimentally driven field, it is natural to seek a theory of…

  • Identifying metric structures of deep latent variable models

    Identifying metric structures of deep latent variable models arXiv:2502.13757v1 Announce Type: new Abstract: Deep latent variable models learn condensed representations of data that, hopefully, reflect the inner workings of the studied phenomena. Unfortunately, these latent representations are not statistically identifiable, meaning they cannot be uniquely determined. Domain experts, therefore, need to tread carefully when interpreting…

  • Learning How to Play Atari Games Through Deep Neural Networks

    Learning How to Play Atari Games Through Deep Neural Networks In July 1959, Arthur Samuel developed one of the first agents to play the game of checkers. What constitutes an agent that plays checkers can be best described in Samuel’s own words, “…a computer [that] can be programmed so that it will learn to play…

  • dynoGP: Deep Gaussian Processes for dynamic system identification

    dynoGP: Deep Gaussian Processes for dynamic system identification arXiv:2502.05620v1 Announce Type: new Abstract: In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are constructed by interconnecting linear dynamic GPs (equivalent to stochastic linear time-invariant dynamical systems) and…

  • Machine Learning Incidents in AdTech

    Machine Learning Incidents in AdTech Source: https://unsplash.com/photos/a-couple-of-signs-that-are-on-a-fence-xXbQIrWH2_A Challenges with deep learning in production One of the biggest challenges I encountered in my career as a data scientist was migrating the core algorithms in a mobile AdTech platform from classic machine learning models to deep learning. I worked on a Demand Side Platform (DSP) for user…

  • Deep Learning for Click Prediction in Mobile AdTech

    Deep Learning for Click Prediction in Mobile AdTech Source: https://pixabay.com/illustrations/rays-stars-light-explosion-galaxy-9350519/ Machine Learning for Real-Time Bidding The past few years were a revolution for the mobile advertising and gaming industries, with the broad adoption of neural networks for advertising tasks, including click prediction. This migration occurred prior to the success of Large Language Models (LLMs) and…

  • On the Statistical Capacity of Deep Generative Models

    On the Statistical Capacity of Deep Generative Models arXiv:2501.07763v1 Announce Type: new Abstract: Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not well understood. A common assumption is that with enough training data and sufficiently large neural networks, deep generative model samples…

  • Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression

    Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression arXiv:2501.04898v1 Announce Type: new Abstract: We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features learned by deep neural networks in two stages. We prove that the DFIV algorithm…

  • Deep Networks are Reproducing Kernel Chains

    Deep Networks are Reproducing Kernel Chains arXiv:2501.03697v1 Announce Type: cross Abstract: Identifying an appropriate function space for deep neural networks remains a key open question. While shallow neural networks are naturally associated with Reproducing Kernel Banach Spaces (RKBS), deep networks present unique challenges. In this work, we extend RKBS to chain RKBS (cRKBS), a new…

  • Deep Learning-based Approaches for State Space Models: A Selective Review

    Deep Learning-based Approaches for State Space Models: A Selective Review arXiv:2412.11211v1 Announce Type: new Abstract: State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the observations. This paper provides…

  • DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations

    DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations arXiv:2412.09687v1 Announce Type: cross Abstract: Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial on resource-constrained devices. To achieve high accuracy, existing methods for quantizing activations…

  • Distribution free uncertainty quantification in neuroscience-inspired deep operators

    Distribution free uncertainty quantification in neuroscience-inspired deep operators arXiv:2412.09369v1 Announce Type: new Abstract: Energy-efficient deep learning algorithms are essential for a sustainable future and feasible edge computing setups. Spiking neural networks (SNNs), inspired from neuroscience, are a positive step in the direction of achieving the required energy efficiency. However, in a bid to lower the…

  • Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering

    Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering arXiv:2412.02292v1 Announce Type: new Abstract: Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we…

  • deep end of the pool…

    https://davidnugent.net/he-ai-2024 https://www.nextplatform.com/2024/10/29/hpc-gets-a-reconfigurable-dataflow-engine-to-take-on-cpus-and-gpus/