Business & Operations

Operations, strategy, finance, sales, support, management, and planning

skills in category: 29,837
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Xoresnet Deep Snn LearningAXOResNet: Exclusive-OR Meta-Residuals for Deep Spiking Neural Networks. Novel architecture addressing spike redundancy and information loss in SNN residual learning. OR-ADD shortcut connection merges branch outputs; XOR meta-residuals select pre-learning residuals to mitigate redundant learning. Outperforms SOTA deep SNNs on Fashion-MNIST, CIFAR-10/100, miniImageNet. Use when: deep SNN architecture, residual learning SNN, spike redundancy mitigation, neuromorphic systems, XOR operations in ne...businesspythongoVotes: 0GitHub stars: 3
Vs Wno Variable Spiking WaveletAVariable Spiking Wavelet Neural Operator (VS-WNO) — a systematic study of spiking sparsity versus real-world deployment cost on edge GPUs. Covers wavelet neural operators augmented with spiking mechanisms, variable sparsity control, hardware-aware model design, and deployment profiling on NVIDIA Jetson Orin Nano 8GB.businesspythongoVotes: 0GitHub stars: 3
Veclstm Trajectory Data Processing And Management For Activity Recognition Through Lstm Vectorization And Database IntegrationA**arXiv ID:** 2409.19258 **Authors:** Solmaz Seyed Monir, Dongfang Zhao **Published:** 2024-09-28T06:22:44Z **Abstract:** Activity recognition is a challenging task due to the large scale of trajectory data and the need for prompt and efficient processing. Existing methods have attempted to mitigate this problem by employing traditional LSTM architectures, but these approaches often suffer from inefficiencies in processing large datasets. In response to this challenge, we propose VecLSTM, a n...businesssqldatabaseVotes: 0GitHub stars: 3
Variational Phasor Circuits BciAVariational Phasor Circuits (VPC) for phase-native Brain-Computer Interface classification using continuous S1 unit circle manifold with trainable phase shifts and unitary mixingbusinessgoVotes: 0GitHub stars: 3
The Neurosymbolic BrainA**arXiv ID:** 2205.13440 **Authors:** Robert Lizée **Published:** 2022-05-13T00:39:19Z **Abstract:** Neural networks promote a distributed representation with no clear place for symbols. Despite this, we propose that symbols are manufactured simply by training a sparse random noise as a self-sustaining attractor in a feedback spiking neural network. This way, we can generate many of what we shall call prime attractors, and the networks that support them are like registers holding a symbolic v...businessVotes: 0GitHub stars: 3
The Costs And Benefits Of Goaldirected Attention In Deep Convolutional Neural NetworksA**arXiv ID:** 2002.02342 **Authors:** Xiaoliang Luo, Brett D. Roads, Bradley C. Love **Published:** 2020-02-06T16:42:00Z **Abstract:** People deploy top-down, goal-directed attention to accomplish tasks, such as finding lost keys. By tuning the visual system to relevant information sources, object recognition can become more efficient (a benefit) and more biased toward the target (a potential cost). Motivated by selective attention in categorisation models, we developed a goal-directed attent...businessgoVotes: 0GitHub stars: 3
Spiking Rl Neuromorphic Robot ControlASpiking reinforcement learning on neuromorphic hardware for real-time robotic control. Uses fixed random connectivity for temporal structure capture and local e-prop learning rule for efficient online learning. Activation: spiking RL robot control, neuromorphic reinforcement learning, e-prop robot, Loihi robot control, air hockey spiking neural network.businessgogitVotes: 0GitHub stars: 3
Spiking Neural Network AnalysisAAnalyze Spiking Neural Network (SNN) papers, extract technical patterns from knowledge graph, and identify reusable research methodologies for neuromorphic computing.businessgobashVotes: 0GitHub stars: 3
Spikenas A Fast Memoryaware Neural Architecture Search Framework For Spiking Neural Networkbased Embedded Ai SystemsA**arXiv ID:** 2402.11322 **Authors:** Rachmad Vidya Wicaksana Putra, Muhammad Shafique **Published:** 2024-02-17T16:33:54Z **Abstract:** Embedded AI systems are expected to incur low power/energy consumption for solving machine learning tasks, as these systems are usually power constrained (e.g., object recognition task in autonomous mobile agents with portable batteries). These requirements can be fulfilled by Spiking Neural Networks (SNNs), since their bio-inspired spike-based operations of...businessgoVotes: 0GitHub stars: 3
Snnf Near Sensor Dvs Noise FilterASNNF: SNN-based Near-Sensor Noise Filter for Dynamic Vision Sensors. Hardware-efficient BA noise filtering using compact EBBI representation, parallel memory architecture, and single-layer SNN classifier. Achieves AUC 0.89 with ~11% memory and ~40% logic of state-of-the-art filters. Ideal for resource-constrained edge DVS applications. Activation: SNNF, DVS noise filter, event-based binary image, background activity noise, near-sensor computing, dynamic vision sensor, EBBI, spatiotemporal fil...businesspythongoVotes: 0GitHub stars: 3
Snn Working Memory Heterogeneous DelaysAWorking memory implementation in recurrent spiking neural networks using heterogeneous synaptic delays. Leverages diverse axonal conduction delays to create temporally distributed representations, enabling persistent activity without continuous stimulation. Use for SNN-based working memory, temporal sequence processing, and delay-dependent neural computation. Activation: working memory SNN, heterogeneous delays, synaptic delay, recurrent SNN memory, temporal representation, delay-based memorybusinesspythongoVotes: 0GitHub stars: 3
Snn Universal ApproximationAUniversal approximation theorem for Spiking Neural Networks (SNNs) with LIF neurons. Use when proving SNN expressiveness, analyzing spike timing encoding, understanding theoretical foundations of SNN approximation power, or studying spike count dynamics across network layers. Provides mathematical framework for SNN function approximation and dynamical constraints.businessgoexpressVotes: 0GitHub stars: 3
Snn Mcu Fullfeature EdgeAFull-feature Spiking Neural Network simulation on microcontrollers for edge neuromorphic applications. Enables ultra-low power SNN deployment on resource-constrained devices. Triggers: SNN, microcontroller, edge computing, neuromorphic, MCU.businessnodedebuggingVotes: 0GitHub stars: 3
Shared State ArchitectureAPSI (Persistent Shared Interface): A shared-state architecture for coherent AI-generated instruments in personal AI agents. Addresses the problem of isolated AI tools by introducing a personal-context bus for cross-module reasoning and synchronized actions. Use when designing multi-agent systems, personal AI environments, tool orchestration, or building coherent AI software architectures.businesspythongoVotes: 0GitHub stars: 3
Rogue Variable CognitionARogue Variable Theory (RVT) for quantum-compatible cognition modeling. Formalizes pre-event cognitive states as structured Rogue Variables embedded in graph Hilbert space via Mirrored Personal Graph (MPG). Includes Rosetta Stone Layer for cross-user latent space alignment. Use when: (1) modeling pre-decision cognitive states, (2) analyzing ambiguity and contextual tension in cognition, (3) building cross-user representation alignment systems, (4) implementing quantum-consistent information-th...businessnodeVotes: 0GitHub stars: 3
Robust Lagrangian And Adversarial Policy Gradient For Robust Constrained Markov Decision ProcessesA**arXiv ID:** 2308.11267 **Authors:** David M. Bossens **Published:** 2023-08-22T08:24:45Z **Abstract:** The robust constrained Markov decision process (RCMDP) is a recent task-modelling framework for reinforcement learning that incorporates behavioural constraints and that provides robustness to errors in the transition dynamics model through the use of an uncertainty set. Simulating RCMDPs requires computing the worst-case dynamics based on value estimates for each state, an approach which ...businessgoperformanceVotes: 0GitHub stars: 3
Quantum Neural ArchitectureAQuantum Neural Network (QNN) architecture design and optimization patterns. Covers quantum-classical hybrid learning, Lie algebra truncation, barren plateau mitigation, quantum expressivity, and tensor network approaches. Activates for: QNN design, quantum neural network, quantum machine learning, quantum-classical hybrid, quantum expressivity phase transition, LieTrunc, quantum gradient descent.businesspythongoVotes: 0GitHub stars: 3
Quantum Hybrid Neural ComputingAQuantum-hybrid neural computing framework for designing and implementing hybrid quantum-classical neural networks. Covers variational quantum circuits (VQC), parameterized quantum circuits (PQC), quantum neural networks (QNN), and hybrid training strategies. Use when implementing quantum-classical ML models, optimizing quantum circuits for neural tasks, or analyzing quantum advantage in deep learning.businesspythongoVotes: 0GitHub stars: 3
Quantum Circuit Synthesis GstAGenerative quantum circuit synthesis from Gate Set Tomography (GST) data using diffusion models and set-vision transformers. Bypasses traditional two-step pipeline (GST characterization + unitary decomposition) by directly learning generative concept spaces from raw GST data. Use when synthesizing hardware-native quantum circuits, learning from gate characterization data, or building context-aware quantum compilation pipelines. Activation: quantum circuit synthesis, GST circuit generation, ha...businesspythonVotes: 0GitHub stars: 3
Qds Snn Quantum Deeply Supervised SpikingAQuantum Deeply-Supervised Spiking Neural Network (QDS-SNN) methodology for energy-efficient traffic sign recognition. Integrates QNNs with SNNs using TSA-LIF neurons and QACM module, achieving 99.72% accuracy with 55.77% energy reduction.businesspythongoVotes: 0GitHub stars: 3
Qb Lif Quantized Burst Neurons V2AQuantized Burst-LIF (QB-LIF) v2 - Enhanced learnable-scale quantized burst neurons for efficient Spiking Neural Networks. Adaptive spike count control with binary activation for hardware deployment. Keywords: SNN, burst neurons, quantization, neuromorphic hardware.businesspythonVotes: 0GitHub stars: 3
Physical Neural Computing ReviewAComprehensive review of physical neural computing substrates beyond silicon: memristive devices, photonic circuits, mechanical metamaterials, microfluidic networks, and chemical reaction systems. Use when designing neuromorphic hardware, evaluating physical substrate alternatives, or researching energy-efficient AI deployment at the edge. Triggers: physical neural computing, neuromorphic substrate, memristor neural networks, photonic neural networks, analog AI, edge AI hardware, non-silicon n...businessgoreactVotes: 0GitHub stars: 3
Persistent Homology Brain Network ControlAMethodology for applying persistent homology to brain network control theory, revealing how topological features broaden the controllable subspace beyond what scalar energy measures capture. Use when analyzing brain structural connectomes, network control theory, or topological data analysis in neuroscience contexts.businesspythongoVotes: 0GitHub stars: 3
Parallel Scan Neural Quantum StatesAParallel Scan Recurrent Neural Quantum States (PSR-NQS) methodology for scalable variational Monte Carlo simulations. Use when designing efficient RNN-based quantum state ansätze, training neural quantum states with autoregressive models, scaling quantum simulations to large 2D spin lattices, or applying parallel scan techniques to sequential quantum architectures.businesspythongitVotes: 0GitHub stars: 3