Deep
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
arXiv:2606.08218v1 Announce Type: new Abstract: Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are a canonical example. In the wide-network limit, the prior is a Gaussian process with a depth-dependent kernel, and its behaviour as depth grows has been extensively studied through this kernel. Here, we study another case, where each layer itself is a vector valued Gaussian process, and our aim is...
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Announce Type: new Abstract: Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep UDA due to the absence of accurate and standardized model selection method, posing an obstacle to further advances in the field. Existing model selection methods for Deep UDA are either highly biased, restricted, unstable, or...
XOResNet: Exclusive-OR Meta-Residuals Facilitate Deep Spiking Neural Networks Learning
arXiv:2605.30362v1 Announce Type: new Abstract: Spiking neural networks (SNNs) hold promise for demonstrating superior learning and representation capabilities in deep models. Given the tremendous success of ResNet in deep learning, it would naturally follow to train deep SNNs with residual learning. However, existing residual structures for constructing deep SNNs still present challenges of spike redundancy or information loss, as well as redundant learning.
Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer
arXiv:2408.11266v5 Announce Type: replace Abstract: Deep learning is now common across many scientific fields, including the study of partial differential equations. This article provides a brief, accessible introduction to core deep learning concepts, including neural networks, backpropagation, and the universal approximation theorem. It mainly covers how to use deep learning in solving differential equations.
Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
arXiv:2605.29861v2 Announce Type: replace Abstract: Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into long-form reports. However, verifiable multimodal deep research remains challenging due to open-ended synthesis without deterministic ground truth and the need to interleave textual arguments with visual evidence. We propose Ptah, a multi-agent harness for...
Consistency Deep Equilibrium Models
arXiv:2602.03024v2 Announce Type: replace Abstract: Deep Equilibrium Models (DEQs) have emerged as a powerful paradigm in deep learning, offering the ability to model infinite-depth networks with constant memory usage. However, DEQs incur significant inference latency due to the iterative nature of fixed-point solvers. In this work, we introduce the Consistency Deep Equilibrium Model (C-DEQ), a novel framework that leverages consistency distillation to accelerate DEQ inference.
Beyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing
arXiv:2606.03666v1 Announce Type: new Abstract: Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning architectures. However, most CS approaches predominantly confine their inference to a single solution space, neglecting the inherent ill-posedness of CS problems that intrinsically permits multiple plausible candidate hypotheses. In this paper, a novel Multi-Hypothesis Collaborative Deep Unfolding CS...
Scientists confirm a deep earthquake that shouldn't exist
Scientists confirm a deep earthquake that shouldn't exist Scientists have uncovered a hidden class of deep-mantle earthquakes beneath Utah, overturning decades of assumptions about where earthquakes can occur. - Date: - June 3, 2026 - Source: - University of Utah - Summary: - Scientists have confirmed that a mysterious Utah earthquake first detected in 1979 really did occur nearly 90 kilometers underground—far deeper than anyone thought earthquakes could happen beneath a continent.
Deep-sea supergiant isopods last years without food by using a two-part survival system
Deep-sea supergiant isopods last years without food by using a two-part survival system Sadie Harley Scientific Editor Robert Egan Associate Editor The supergiant bathynomid is a deep-sea isopod famous for surviving more than five years without food. Despite residing in an extremely low-nutrient habitat, these organisms exhibit pronounced body gigantism, a trait that requires substantial energy. This raises an energy paradox: How do these apparently energy-hungry isopods sustain their...
Mining companies may soon bypass UN rules and mine the deep sea
Mining companies may soon bypass UN rules and mine the deep sea Lisa Lock Scientific Editor Andrew Zinin Lead Editor A Canadian deep-sea mining company may become the first to commercially mine the international seabed under a controversial U.S. executive order that bypasses United Nations regulations. A recent legal analysis suggests that this could place Canada in violation of international law. The U.S. National Oceanic and Atmospheric Administration (NOAA) recently announced that an...