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CLI tools, utilities, converters, workflow automation, and productivity helpers
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Showing 17,545–17,568 of 49,285 skills
- Approximation Theory For Neural Networks Old And New**arXiv ID:** 2605.21451 **Authors:** Soumendu Sundar Mukherjee, Himasish Talukdar **Published:** 2026-05-20T17:42:34Z **Abstract:** Universal approximation theorems provide a mathematical explanation for the expressive power of neural networks. They assert that, under mild conditions on the activation function, feedforward neural networks are dense in broad function classes, such as continuous functions on compact subsets of $\mathbb{R}^d$, $L^p$ spaces, or Sobolev spaces. Over the past four...Votes: 0GitHub stars: 3
- Approximate Spectral Clustering With Eigenvector Selection And Selftuned K**arXiv ID:** 2302.11297 **Authors:** Mashaan Alshammari, Masahiro Takatsuka **Published:** 2023-02-22T11:32:24Z **Abstract:** The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption. Unfortunately, with a computational complexity of $O(n^3)$, it was infeasible for multiple real applications, where $n$ could be large. This stimulates researchers to propose the approximate spectral clustering (ASC). ...Votes: 0GitHub stars: 3
- Advanced Displacement Magnitude Prediction In Multimaterial Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture**arXiv ID:** 2501.03254 **Authors:** Akshansh Mishra **Published:** 2024-12-31T00:15:58Z **Abstract:** This paper proposes an innovative method for predicting deformation in architected lattice structures that combines Physics-Informed Neural Networks (PINNs) with finite element analysis. A thorough study was carried out on FCC-based lattice beams utilizing five different materials (Structural Steel, AA6061, AA7075, Ti6Al4V, and Inconel 718) under varied edge loads (1000-10000 N). The PINN m...Votes: 0GitHub stars: 3
- Why Does Ctc Result In Peaky Behavior**arXiv ID:** 2105.14849 **Authors:** Albert Zeyer, Ralf Schlüter, Hermann Ney **Published:** 2021-05-31T10:03:14Z **Abstract:** The peaky behavior of CTC models is well known experimentally. However, an understanding about why peaky behavior occurs is missing, and whether this is a good property. We provide a formal analysis of the peaky behavior and gradient descent convergence properties of the CTC loss and related training criteria. Our analysis provides a deep understanding why peaky beh...Votes: 0GitHub stars: 3
- When Fireflies Cluster Enhancing Automatic Clustering Via Centroidguided Firefly Optimization**arXiv ID:** 2605.18460 **Authors:** MKA Ariyaratne, Azwirman Gusrialdi, Yury Nikulin, Jaakko Peltonen **Published:** 2026-05-18T14:22:13Z **Abstract:** This work presents a novel variant of the Firefly Algorithm (FA) for data clustering, addressing limitations of traditional methods like K-Means that struggle with non-uniform cluster shapes, densities, and the need for pre-defining the number of clusters. The proposed algorithm introduces a centroid movement strategy and a multi-objective f...Votes: 0GitHub stars: 3
- What Are Neural Networks Made Of**arXiv ID:** 1909.09588 **Authors:** Rene Schaub **Published:** 2019-08-25T21:59:26Z **Abstract:** The success of Deep Learning methods is not well understood, though various attempts at explaining it have been made, typically centered on properties of stochastic gradient descent. Even less clear is why certain neural network architectures perform better than others. We provide a potential opening with the hypothesis that neural network training is a form of Genetic Programming.Votes: 0GitHub stars: 3
- Trajectory Based Controlled InvariantsCompute controlled invariant sets for linear discrete-time systems using trajectory-based approach with convex feasible points. Use for MPC design, control invariant set computation, feasibility guarantees, and discrete-time system analysis. Keywords: controlled invariant, MPC, trajectory-based, convex feasible points, discrete-time systems, recursive feasibility.Votes: 0GitHub stars: 3
- Toward Practical Equilibrium Propagation Braininspired Recurrent Neural Network With Feedback Regulation And Residual Connections**arXiv ID:** 2508.11659 **Authors:** Zhuo Liu, Tao Chen **Published:** 2025-08-05T15:07:50Z **Abstract:** Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing im-plementations of EP suffer from instability and prohibi-tively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically...Votes: 0GitHub stars: 3
- Topographic Vaes Learn Equivariant Capsules**arXiv ID:** 2109.01394 **Authors:** T. Anderson Keller, Max Welling **Published:** 2021-09-03T09:25:57Z **Abstract:** In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. We show that such a model indeed learns to organize its activations according to salient characteristi...Votes: 0GitHub stars: 3
- Tool Integrated Reasoning RecipeFull-pipeline recipe for injecting tool-use into thinking models without sacrificing text-only reasoning. Two-stage SFT+RL approach. Based on arXiv 2605.06326.Votes: 0GitHub stars: 3
- Think Short Defer Smart Act And Repeat CalibratedThink Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for EVotes: 0GitHub stars: 3
- The Relationship Between Biological And Artificial Intelligence**arXiv ID:** 1905.00547 **Authors:** George Cevora **Published:** 2019-05-01T13:41:35Z **Abstract:** Intelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given tech...Votes: 0GitHub stars: 3
- Tanhsoft A Family Of Activation Functions Combining Tanh And Softplus**arXiv ID:** 2009.03863 **Authors:** Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey **Published:** 2020-09-08T16:59:28Z **Abstract:** Deep learning at its core, contains functions that are composition of a linear transformation with a non-linear function known as activation function. In past few years, there is an increasing interest in construction of novel activation functions resulting in better learning. In this work, we propose a family of novel activation function...Votes: 0GitHub stars: 3
- Synergy Between Synaptic Consolidation And Experience Replay For General Continual Learning**arXiv ID:** 2206.04016 **Authors:** Fahad Sarfraz, Elahe Arani, Bahram Zonooz **Published:** 2022-06-08T17:08:56Z **Abstract:** Continual learning (CL) in the brain is facilitated by a complex set of mechanisms. This includes the interplay of multiple memory systems for consolidating information as posited by the complementary learning systems (CLS) theory and synaptic consolidation for protecting the acquired knowledge from erasure. Thus, we propose a general CL method that creates a syner...Votes: 0GitHub stars: 3
- Streamhoi Interaction Aware Temporal Memory AdaptaSkill generated from arXiv paper 2607.20174: StreamHOI: Interaction-aware Temporal Memory Adaptation for Streaming HOI Video GenerationVotes: 0GitHub stars: 3
- Spectralot Functional AlignmentMethod for geometry-aware functional alignment of fMRI data using SpectralOT to improve cross-subject decoding by embedding cortical geometry into Laplace-Beltrami eigenmodes.Votes: 0GitHub stars: 3
- Spark Modular Spiking Neural Networks**arXiv ID:** 2602.02306 **Authors:** Mario Franco, Carlos Gershenson **Published:** 2026-02-02T16:36:58Z **Abstract:** Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several alternative forms of neural networks have been proposed to address some of these problems. Specifically, spiking neural networks are suitable for efficient hardware implementations. Howe...Votes: 0GitHub stars: 3
- Spacetime Continuous Pde Forecasting Using Equivariant Neural Fields**arXiv ID:** 2406.06660 **Authors:** David M. Knigge, David R. Wessels, Riccardo Valperga, Samuele Papa, Jan-Jakob Sonke, Efstratios Gavves, Erik J. Bekkers **Published:** 2024-06-10T11:49:11Z **Abstract:** Recently, Conditional Neural Fields (NeFs) have emerged as a powerful modelling paradigm for PDEs, by learning solutions as flows in the latent space of the Conditional NeF. Although benefiting from favourable properties of NeFs such as grid-agnosticity and space-time-continuous dynamics ...Votes: 0GitHub stars: 3
- Socrates Loss Unifying Confidence Calibration And Classification By Leveraging The Unknown**arXiv ID:** 2604.12245 **Authors:** Sandra Gómez-Gálvez, Tobias Olenyi, Gillian Dobbie, Katerina Taškova **Published:** 2026-04-14T03:43:15Z **Abstract:** Deep neural networks, despite their high accuracy, often exhibit poor confidence calibration, limiting their reliability in high-stakes applications. Current ad-hoc confidence calibration methods attempt to fix this during training but face a fundamental trade-off: two-phase training methods achieve strong classification performance at th...Votes: 0GitHub stars: 3
- Slim Lstms**arXiv ID:** 1812.11391 **Authors:** Fathi M. Salem **Published:** 2018-12-29T16:11:01Z **Abstract:** Long Short-Term Memory (LSTM) Recurrent Neural networks (RNNs) rely on gating signals, each driven by a function of a weighted sum of at least 3 components: (i) one of an adaptive weight matrix multiplied by the incoming external input vector sequence, (ii) one adaptive weight matrix multiplied by the previous memory/state vector, and (iii) one adaptive bias vector. In effect, they augment t...Votes: 0GitHub stars: 3
- Slai T Rex Full Parameter Post Training Of The DeeSkill generated from arXiv paper 2607.20145: SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPODVotes: 0GitHub stars: 3
- Shunting Inhibition Dendritic CreditShunting inhibition and dendritic branching reshape local credit assignment geometry. Shows how E/I conductance + dendritic tree structure enable biological neurons to approximate backprop with restricted somatic feedback. By Safaai, Richards & Sabatini (arXiv:2607.03556, July 2026).Votes: 0GitHub stars: 3
- Selfconstructing Neural Networks Through Random Mutation**arXiv ID:** 2103.15692 **Authors:** Samuel Schmidgall **Published:** 2021-03-29T15:27:38Z **Abstract:** The search for neural architecture is producing many of the most exciting results in artificial intelligence. It has increasingly become apparent that task-specific neural architecture plays a crucial role for effectively solving problems. This paper presents a simple method for learning neural architecture through random mutation. This method demonstrates 1) neural architecture may be le...Votes: 0GitHub stars: 3
- Selfattentive Neural Collaborative Filtering**arXiv ID:** 1806.06446 **Authors:** Yi Tay, Shuai Zhang, Luu Anh Tuan, Siu Cheung Hui **Published:** 2018-06-17T20:58:12Z **Abstract:** This paper has been withdrawn as we discovered a bug in our tensorflow implementation that involved accidental mixing of vectors across batches. This lead to different inference results given different batch sizes which is completely strange. The performance scores still remain the same but we concluded that it was not the self-attention that contributed to...Votes: 0GitHub stars: 3