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Operations, strategy, finance, sales, support, management, and planning
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Showing 10,153–10,176 of 29,822 skills
- Competitor AnalysisImported skill competitor_analysis from openaiVotes: 0GitHub stars: 3
- StateImported skill state from langchainVotes: 0GitHub stars: 3
- Sandbox FactoryImported skill sandbox_factory from langchainVotes: 0GitHub stars: 3
- WorkflowsImported skill workflows from anthropicVotes: 0GitHub stars: 3
- ThumbnailImported skill thumbnail from anthropicVotes: 0GitHub stars: 3
- ReplaceImported skill replace from anthropicVotes: 0GitHub stars: 3
- ReferenceImported skill reference from anthropicVotes: 0GitHub stars: 3
- RearrangeImported skill rearrange from anthropicVotes: 0GitHub stars: 3
- Python Mcp ServerImported skill python_mcp_server from anthropicVotes: 0GitHub stars: 3
- Node Mcp ServerImported skill node_mcp_server from anthropicVotes: 0GitHub stars: 3
- InventoryImported skill inventory from anthropicVotes: 0GitHub stars: 3
- Desert RoseImported skill desert_rose from anthropicVotes: 0GitHub stars: 3
- Exploring Layerwise Adversarial Robustness Through The Lens Of Tsne**arXiv ID:** 2406.14073 **Authors:** Inês Valentim, Nuno Antunes, Nuno Lourenço **Published:** 2024-06-20T07:50:11Z **Abstract:** Adversarial examples, designed to trick Artificial Neural Networks (ANNs) into producing wrong outputs, highlight vulnerabilities in these models. Exploring these weaknesses is crucial for developing defenses, and so, we propose a method to assess the adversarial robustness of image-classifying ANNs. The t-distributed Stochastic Neighbor Embedding (t-SNE) techniqu...Votes: 0GitHub stars: 3
- Editbridge Ultra Hdr Image EditingFaithful 4K image editing via diffusion bridge framework.Votes: 0GitHub stars: 3
- Yana Neuromorphic SimulationGPU-accelerated neuromorphic simulation methodology with thousands of neurons on a single GPU. Enables large-scale spiking neural network simulation for connectomic-scale neural circuits. Trigger words: yana, neuromorphic simulation, gpu-accelerated snn, large-scale spiking simulation, connectomic simulation, thousands neurons gpu, spiking neural network scaling.Votes: 0GitHub stars: 3
- Vo2 Mott Spiking Neuron HardwareVO2 Mott oscillator-based spiking neuron hardware for neuromorphic computing. Monolithic CMOS-BEOL integration of energy-efficient spiking neurons using vanadium dioxide phase-transition materials. Activation: vo2, mott, spiking neuron, neuromorphic hardware, phase-transition, BEOL integration.Votes: 0GitHub stars: 3
- Spikingmot Spike Driven Multi Object TrackerSpikingMOT: A Spike-Driven Multi-Object Tracker that uses brain-inspired spiking neural networks for efficient trajectory prediction and target association. Achieves state-of-the-art performance while reducing parameters by 72% and energy by 86.7%. Use when working with multi-object tracking, spiking neural networks, or efficient computer vision applications.Votes: 0GitHub stars: 3
- Spiking Bandpass Wavelet EncodingSpiking Bandpass Wavelet encoding methodology for temporal signal processing using spike-based representations. Recasts spike encoders as time-causal wavelet frames with quantitative bandwidths and reconstruction error bounds. Preserves sparsity and locality of spiking representations, with direct mapping to neuromorphic hardware. Use when: spike-based signal encoding, neuromorphic signal processing, spiking wavelet transforms, temporal signal encoding/decoding, energy-efficient spike encodin...Votes: 0GitHub stars: 3
- Spikemllm Multimodal SpikingSpikeMLLM: First spike-based Multimodal Large Language Model (MLLM) framework. Unifies ANN quantization methods into spike representation space via Modality-Specific Temporal Scales (MSTS) and Temporal Compression LIF (TC-LIF) neurons. Compresses timestep from T=L-1 to T=log2(L)-1 while maintaining near-lossless performance across four MLLMs. Enables energy-efficient multimodal inference through spike-based computation. 首个基于脉冲的多模态大语言模型框架，通过模态特定时间尺度和时间压缩LIF神经元实现高效多模态推理。Votes: 0GitHub stars: 3
- Spectral Theory Population Density Spiking NeuronsSpectral theory framework for analyzing population density dynamics of spiking neurons with refractoriness. Provides rigorous mathematical foundation for spectral decomposition methods in computational neuroscience by formulating the problem as a non-self-adjoint boundary eigenvalue problem for the Fokker-Planck operator.Votes: 0GitHub stars: 3
- Rippled Graphene Fluidic Memristor NeuromorphicNanofluidic memristive devices using rippled graphene pores for ionic memory, synaptic plasticity emulation, and neuromorphic circuit design. Covers graphene pore engineering, ion-selective memory effects, programmable conductance modification via voltage spikes, and integrated ionic circuits for image identification and neural signal analysis.Votes: 0GitHub stars: 3
- Qslm A Performance And Memoryaware Quantization Framework With Tiered Search Strategy For Spikedriven Language Models**arXiv ID:** 2601.00679 **Authors:** Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique **Published:** 2026-01-02T13:05:33Z **Abstract:** Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (e.g., accuracy) and capabilities in generating high-quality responses to the given inputs. However, their large computational cost, huge memory footprints, and high processing power/energy make ...Votes: 0GitHub stars: 3
- Knowing When To Stop Delayadaptive Spiking Neural Network Classifiers With Reliability Guarantees**arXiv ID:** 2305.11322 **Authors:** Jiechen Chen, Sangwoo Park, Osvaldo Simeone **Published:** 2023-05-18T22:11:04Z **Abstract:** Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics. The energy consumption of an SNN depends on the number of spikes exchanged between neurons over the course of the input presentation. Typically, decisions are produced after the entire input sequence has been processed. This results in latency and energy consumption...Votes: 0GitHub stars: 3
- Focus Session Hardware And Software Techniques For Accelerating Multimodal Foundation Models**arXiv ID:** 2604.21952 **Authors:** Muhammad Shafique, Abdul Basit, Muhammad Abdullah Hanif, Alberto Marchisio, Rachmad Vidya Wicaksana Putra, Minghao Shao **Published:** 2026-04-23T05:27:39Z **Abstract:** This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines hardware and software co-design of transformer blocks with an optimization pipeline that reduces computational and memory requirements. During model development, it...Votes: 0GitHub stars: 3