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CLI tools, utilities, converters, workflow automation, and productivity helpers
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- Self Modifying Lean Proof Agents With Verifier GroDerived from arXiv:2607.17352 - Self-Modifying Lean Proof Agents with Verifier-Grounded Benchmark CoevolutionVotes: 0GitHub stars: 3
- Selected Trends In Artificial Intelligence For Space Applications**arXiv ID:** 2212.06662 **Authors:** Dario Izzo, Gabriele Meoni, Pablo Gómez, Dominik Dold, Alexander Zoechbauer **Published:** 2022-12-10T07:49:50Z **Abstract:** The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced. As more and more aerospace engineers are becoming aware of new trends in AI, traditional approaches are revisited to consider the applications of emergin...Votes: 0GitHub stars: 3
- Scores Are Not Decisions Cost Aware Stopping For TScores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM AgentsVotes: 0GitHub stars: 3
- Scaling Up Estimation Of Distribution Algorithms For Continuous Optimization**arXiv ID:** 1111.2221 **Authors:** Weishan Dong, Tianshi Chen, Peter Tino, Xin Yao **Published:** 2011-11-09T14:44:58Z **Abstract:** Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs' performance in the context of global optimization. So far, the studies or applications of multivariate probabilistic model based continuous EDAs are still restricted to rather low dimensional problems (smaller than 100D). Traditional EDAs have difficu...Votes: 0GitHub stars: 3
- Scaling Laws Beyond Backpropagation**arXiv ID:** 2210.14593 **Authors:** Matthew J. Filipovich, Alessandro Cappelli, Daniel Hesslow, Julien Launay **Published:** 2022-10-26T10:09:14Z **Abstract:** Alternatives to backpropagation have long been studied to better understand how biological brains may learn. Recently, they have also garnered interest as a way to train neural networks more efficiently. By relaxing constraints inherent to backpropagation (e.g., symmetric feedforward and feedback weights, sequential updates), these m...Votes: 0GitHub stars: 3
- Safeapt Safe Simulationtoreal Robot Learning Using Diverse Policies Learned In Simulation**arXiv ID:** 2201.13248 **Authors:** Rituraj Kaushik, Karol Arndt, Ville Kyrki **Published:** 2022-01-27T16:40:36Z **Abstract:** The framework of Simulation-to-real learning, i.e, learning policies in simulation and transferring those policies to the real world is one of the most promising approaches towards data-efficient learning in robotics. However, due to the inevitable reality gap between the simulation and the real world, a policy learned in the simulation may not always generate a sa...Votes: 0GitHub stars: 3
- Rethinking Self Evolution A Constrained ExploratioRethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill OverfVotes: 0GitHub stars: 3
- Research Paper Pattern ExtractorExtract reusable research skill patterns from knowledge graph paper analysis. Uses PageRank, Louvain, and vector search to identify important papers and research clusters, then distills patterns into new skills. Activation: extract research pattern, research skill extractor, 研究模式提炼, paper pattern analysis.Votes: 0GitHub stars: 3
- Reliability Testing For Natural Language Processing Systems**arXiv ID:** 2105.02590 **Authors:** Samson Tan, Shafiq Joty, Kathy Baxter, Araz Taeihagh, Gregory A. Bennett, Min-Yen Kan **Published:** 2021-05-06T11:24:58Z **Abstract:** Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need ...Votes: 0GitHub stars: 3
- Random Projection Tree Similarity Metric For Spectralnet**arXiv ID:** 2302.13168 **Authors:** Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed, Masahiro Takatsuka **Published:** 2023-02-25T21:32:16Z **Abstract:** SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with $k$-nn graphs, which are usually constructed using a distance metric (e.g., Euclidean distance). $k$-nn graphs restrict the points to have a fixed number of neighbors regardless of the local stati...Votes: 0GitHub stars: 3
- Radon Sobolev Variational Autoencoders**arXiv ID:** 1911.13135 **Authors:** Gabriel Turinici **Published:** 2019-11-29T15:02:28Z **Abstract:** The quality of generative models (such as Generative adversarial networks and Variational Auto-Encoders) depends heavily on the choice of a good probability distance. However some popular metrics like the Wasserstein or the Sliced Wasserstein distances, the Jensen-Shannon divergence, the Kullback-Leibler divergence, lack convenient properties such as (geodesic) convexity, fast evaluation a...Votes: 0GitHub stars: 3
- Quantum Qubit Measurement AnalysisQuantum qubit measurement and state transition analysis methods for circuit QED systems, including fluxonium qubits, measurement-induced transitions, and multi-photon resonance analysis. Activates on: qubit measurement, fluxonium analysis, quantum readout, measurement-induced transition, quantum bit, 量子比特, 量子测量, fluxonium qubit.Votes: 0GitHub stars: 3
- Quantum Entanglement Pet ImagingMethodology for quantum entanglement degree imaging using PET scanners — extracting C_QE biomarkers from annihilation photon polarization correlations via Compton scattering. Use when researching quantum entanglement medical imaging, PET biomarker development, positronium imaging, or polarization-correlated photon diagnostics.Votes: 0GitHub stars: 3
- Quantum Cloning Learning EquivalenceQuantum cloning-learning equivalence methodology — proves that for structured quantum state classes (n-qubit stabilizer states), the optimal sample complexity of cloning equals that of learning: Theta(n). Uses Abelian State Hidden Subgroup framework and random purification channels to connect quantum cloning to classical sample amplification. Bridges No-Cloning theorem foundations with quantum learning theory and cryptography. Activation: quantum cloning, quantum learning theory, stabilizer s...Votes: 0GitHub stars: 3
- Quadratically Constrained Quadratic Programming For Classification Using Particle Swarms And Applications**arXiv ID:** 1407.6315 **Authors:** Deepak Kumar, A G Ramakrishnan **Published:** 2014-07-23T18:04:23Z **Abstract:** Particle swarm optimization is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The approach of particle swarms is an example for interior point methods in optimization as an iterative technique. This approach is novel and deals with classification problems without th...Votes: 0GitHub stars: 3
- Qtnvqc An Endtoend Learning Framework For Quantum Neural Networks**arXiv ID:** 2110.03861 **Authors:** Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen **Published:** 2021-10-06T14:44:51Z **Abstract:** The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC). The ar...Votes: 0GitHub stars: 3
- Qfi Stabilizer FrameworkQuantum Fisher Information framework for stabilizer codes — constructing nonlocal observables with extensive QFI density for quantum metrology. Maps stabilizer generators to dual Ising spins whose correlators equal string order parameters, converting hidden nonlocal order into metrologically accessible observables. Use when analyzing quantum metrology in stabilizer systems, computing QFI bounds, studying string order parameters, evaluating monitored cluster codes or toric code for sensing, or...Votes: 0GitHub stars: 3
- Pwo Trust Region Nqs OptimizationProximal Wavefunction Optimization (PWO) methodology for training Neural Quantum States using trust-region optimization. Clips probability-ratio changes in amplitude and phase channels, scales NQS training to billion-parameter models without explicit matrix inversion.Votes: 0GitHub stars: 3
- Puda An Ai Native Hardware Harness For Self DrivinPUDA: An AI-Native Hardware Harness for Self-Driving LaboratoriesVotes: 0GitHub stars: 3
- Provable Benefits Of Complex Parameterizations For Structured State Space Models**arXiv ID:** 2410.14067 **Authors:** Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen **Published:** 2024-10-17T22:35:50Z **Abstract:** Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. ...Votes: 0GitHub stars: 3
- Projector Variational AnsatzProjector Variational Ansatz (PVA) methodology for VQE that bridges NISQ variational and FTQC algorithm structures. Combines shallow ansatz depth with projector-based ground state identification.Votes: 0GitHub stars: 3
- Probabilistic Neural Circuits**arXiv ID:** 2403.06235 **Authors:** Pedro Zuidberg Dos Martires **Published:** 2024-03-10T15:25:49Z **Abstract:** Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework for discussing probabilistic models that support tractable queries and are yet expressive enough to model complex probability distributions. Nevertheless, tractability comes at a cost: PCs are less expressive than neural networks. In this paper we introduce probabilistic neural circuits ...Votes: 0GitHub stars: 3
- Prelu Yet Another Singlelayer Solution To The Xor Problem**arXiv ID:** 2409.10821 **Authors:** Rafael C. Pinto, Anderson R. Tavares **Published:** 2024-09-17T01:28:40Z **Abstract:** This paper demonstrates that a single-layer neural network using Parametric Rectified Linear Unit (PReLU) activation can solve the XOR problem, a simple fact that has been overlooked so far. We compare this solution to the multi-layer perceptron (MLP) and the Growing Cosine Unit (GCU) activation function and explain why PReLU enables this capability. Our results show th...Votes: 0GitHub stars: 3
- Predprop Bidirectional Stochastic Optimization With Precision Weighted Predictive Coding**arXiv ID:** 2111.08792 **Authors:** André Ofner, Sebastian Stober **Published:** 2021-11-16T21:43:15Z **Abstract:** We present PredProp, a method for optimization of weights and states in predictive coding networks (PCNs) based on the precision of propagated errors and neural activity. PredProp jointly addresses inference and learning via stochastic gradient descent and adaptively weights parameter updates by approximate curvature. Due to the relation between propagated error covariance and...Votes: 0GitHub stars: 3