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- Arxiv 2609 02272v1 Papercompiler Faithful Paper To Code Generation Vi**arXiv ID:** 2609.02272v1 **Authors:** Yunhao Liu, Hong Phuc Pham, Jaehong Yoon **URL:** http://arxiv.org/abs/2609.02272v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Sram Cim Snn AcceleratorSRAM-Based Compute-in-Memory Accelerator for Linear-decay Spiking Neural Networks. Leverages in-memory computing to eliminate the von Neumann bottleneck for SNN inference using linear-decay neuron models compatible with CIM crossbar arrays for ultra-low power edge inference. Activation: SRAM CIM SNN, compute-in-memory spiking network, linear-decay SNN accelerator, CIM crossbar SNN, in-memory computing spiking neural network.Votes: 0GitHub stars: 3
- Spike Image Decoder从神经脉冲重建视觉场景的深度学习框架（SID）。端到端解码器， 从视网膜神经节细胞脉冲重建静态图像和动态视频。 触发词：神经解码、视觉重建、脉冲解码、脑机接口、神经假体、 neural decoding, visual reconstruction, spike decoding, brain-machine interface。Votes: 0GitHub stars: 3
- Rllogo Deep Reinforcement Learning Localization For Logo Recognition**arXiv ID:** 2312.16792 **Authors:** Masato Fujitake **Published:** 2023-12-28T02:44:28Z **Abstract:** This paper proposes a novel logo image recognition approach incorporating a localization technique based on reinforcement learning. Logo recognition is an image classification task identifying a brand in an image. As the size and position of a logo vary widely from image to image, it is necessary to determine its position for accurate recognition. However, because there is no annotation for...Votes: 0GitHub stars: 3
- Retinomorphic Optical Spiking NeuronHodgkin-Huxley-based optical spiking neuron (OSHN) methodology for energy-efficient retinomorphic vision processing and camouflaged object detection. Uses 2D anti-ambipolar phototransistor in subthreshold regime, emulates retinal center-surround receptive fields, achieves sub-picojoule spike energy.Votes: 0GitHub stars: 3
- Quantum Neuromorphic ArchitecturesDesign patterns for quantum-neuromorphic computing including QHDC mappings, three-layer quantum brain CQEC analysis, LMG Hamiltonian phase transitions, and photonic quantum memristors.Votes: 0GitHub stars: 3
- Quantum Neural States Grand CanonicalNeural network quantum state architecture for grand canonical ensemble simulations. Use when representing symmetric bosonic wavefunctions in Fock space, studying quantum many-body systems with variable particle number, computing one-body reduced density matrices, or estimating condensate fractions and radial density profiles from first principles.Votes: 0GitHub stars: 3
- Quantum Neural Network DesignerDesign and optimize quantum neural network architectures based on Lie algebra truncation and parameterized quantum circuit theory. Use when working with quantum machine learning tasks: (1) Designing QNN architectures for classification/regression, (2) Analyzing trainability and barren plateaus, (3) Optimizing quantum circuit expressivity, (4) Evaluating noise robustness of QNNs. Keywords: quantum neural network, QNN design, quantum circuit, parameterized quantum circuit, LieTrunc-QNN, barren ...Votes: 0GitHub stars: 3
- Inducing Functions Through Reinforcement Learning Without Task Specification**arXiv ID:** 2111.11647 **Authors:** Junmo Cho, Dong-Hwan Lee, Young-Gyu Yoon **Published:** 2021-11-23T04:42:02Z **Abstract:** We report a bio-inspired framework for training a neural network through reinforcement learning to induce high level functions within the network. Based on the interpretation that animals have gained their cognitive functions such as object recognition - without ever being specifically trained for - as a result of maximizing their fitness to the environment, we plac...Votes: 0GitHub stars: 3
- Eeg Benchmarking Needs A Task Specification LayerDerived from arXiv:2606.22925 - EEG Benchmarking Needs a Task Specification Layer: NeuroDoc for Rulebook-Guided, Executable Benchmark ConstructionVotes: 0GitHub stars: 3
- Diffusion Language Models For Speech Recognition**arXiv ID:** 2604.14001 **Authors:** Davyd Naveriani, Albert Zeyer, Ralf Schlüter, Hermann Ney **Published:** 2026-04-15T15:46:15Z **Abstract:** Diffusion language models have recently emerged as a leading alternative to standard language models, due to their ability for bidirectional attention and parallel text generation. In this work, we explore variants for their use in speech recognition. Specifically, we introduce a comprehensive guide to incorporating masked diffusion language models ...Votes: 0GitHub stars: 3
- Neuroinspector Hierarchical Dataset InspectionNeuroInspector framework for local-first inspection and annotation of hierarchical neuroscience datasets (HDF5/NWB files) using browser-based WebAssembly HDF5 parsing. Provides structural navigation, metadata inspection, sampled data previews, and path-level annotation into portable project packs without modifying original files. Use for neuroscience data workflow inspection tasks.Votes: 0GitHub stars: 3
- Unsupervised Learning And Exploration Of Reachable Outcome Space**arXiv ID:** 1909.05508 **Authors:** Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx, Stephane Doncieux **Published:** 2019-09-12T08:47:44Z **Abstract:** Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning process. In such situations, a good search strategy is fundamental. At the same time, not having to adapt the algorithm to every single problem is very desirable...Votes: 0GitHub stars: 3
- Sr Agent An Experience Driven Agentic Framework FoDerived from arXiv:2607.17719 - SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategies Refinement in E-Commerce RecommendationVotes: 0GitHub stars: 3
- Sd Search On Policy Hindsight DistillationSD-Search methodology for search-augmented reasoning. Derives step-level supervision from the policy itself through on-policy hindsight self-distillation, without external teacher or annotations.Votes: 0GitHub stars: 3
- Saga Synthetic Agentic Graph Architecture For TempDerived from arXiv:2607.17288 - SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark GenerationVotes: 0GitHub stars: 3
- Roser Rl Component SynergyROSER for RL component synergy in sample-efficient control.Votes: 0GitHub stars: 3
- Reward Learning From Human Preferences And Demonstrations In Atari**arXiv ID:** 1811.06521 **Authors:** Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei **Published:** 2018-11-15T18:33:43Z **Abstract:** To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train...Votes: 0GitHub stars: 3
- Reinforcement Learning From Algorithms To FoundatiDerived from arXiv:2607.17560 - Reinforcement Learning: From Algorithms To Foundation ModelsVotes: 0GitHub stars: 3
- Qftuner Breaking Tradition In Reinforcement Learning**arXiv ID:** 2402.16562 **Authors:** Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid **Published:** 2024-02-26T13:39:04Z **Abstract:** In reinforcement learning algorithms, the hyperparameters tuning method refers to choosing the optimal parameters that may increase the overall performance. Manual or random hyperparameter tuning methods can lead to different results in the reinforcement learning algorithms. In this paper, we propose a new method called QF-tuner for automatic hyperparameter...Votes: 0GitHub stars: 3
- Program Synthesis Through Reinforcement Learning Guided Tree Search**arXiv ID:** 1806.02932 **Authors:** Riley Simmons-Edler, Anders Miltner, Sebastian Seung **Published:** 2018-06-08T00:53:43Z **Abstract:** Program Synthesis is the task of generating a program from a provided specification. Traditionally, this has been treated as a search problem by the programming languages (PL) community and more recently as a supervised learning problem by the machine learning community. Here, we propose a third approach, representing the task of synthesizing a given pro...Votes: 0GitHub stars: 3
- Possibility Before Utility Learning And Using Hierarchical Affordances**arXiv ID:** 2203.12686 **Authors:** Robby Costales, Shariq Iqbal, Fei Sha **Published:** 2022-03-23T19:17:22Z **Abstract:** Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead only consider ones they deem possible in the first place. By focusing only on what is feasible, or "afforded", at the present moment, an agen...Votes: 0GitHub stars: 3
- Hierarchical Deep Reinforcement Learning Integrating Temporal Abstraction And Intrinsic Motivation**arXiv ID:** 1604.06057 **Authors:** Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi, Joshua B. Tenenbaum **Published:** 2016-04-20T18:47:48Z **Abstract:** Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sak...Votes: 0GitHub stars: 3
- Harness Vla Steering Frozen Vlas Memory Guided AgentsMemory-augmented agentic framework that exposes a frozen VLA as a retryable contact-rich primitive composed with analytic primitives. Learns operating range from execution traces and failure models. +38.6pp on LIBERO-Pro, +25.4pp on RoboCasa365. Activation: vision-language-action, memory-guided agents, manipulation primitives, frozen VLA, robot manipulation.Votes: 0GitHub stars: 3