Business & Operations

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

skills in category: 29,837
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Minmaxplus Neural NetworksA**arXiv ID:** 2102.06358 **Authors:** Ye Luo, Shiqing Fan **Published:** 2021-02-12T06:09:20Z **Abstract:** We present a new model of neural networks called Min-Max-Plus Neural Networks (MMP-NNs) based on operations in tropical arithmetic. In general, an MMP-NN is composed of three types of alternately stacked layers, namely linear layers, min-plus layers and max-plus layers. Specifically, the latter two types of layers constitute the nonlinear part of the network which is trainable and more ...businessgoexpressVotes: 0GitHub stars: 3
Meta Learning In Context Brain Decoding V3AMeta-learning in-context brain decoding methodology for zero-shot cross-subject generalization in BCI. Enables training-free adaptation to new users by framing brain signal decoding as an in-context learning problem — constructing support sets from other subjects and using them as context at inference time without any fine-tuning. Applicable to EEG, MEG, fMRI, and invasive recordings.businesspythongoVotes: 0GitHub stars: 3
Iceberg Error DetectionAFault-tolerant error detection using the Iceberg [[2m, 2m-2, 2]] quantum error-detecting code. Implements beyond-break-even error detection for multi-qubit gates on trapped-ion quantum computers. Keywords: quantum error detection, Iceberg code, fault-tolerant, trapped-ion, multi-qubit gates, Toffoli, Bell state, error correction.businessgoperformanceVotes: 0GitHub stars: 3
Hypergraph Neural Stochastic Diffusion An Sde Framework For UncertaintyAHypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored. Unlike pairwise graphs, uncertainty in hypergraphs. Based on arXiv:2607.07330.businessrustgoVotes: 0GitHub stars: 3
Fpga Quantum Error DecoderAFPGA-based real-time quantum error correction decoding architecture. Combines hardware-integrated NN decoders on FPGA with superconducting quantum processors for low-latency closed-loop QEC. Use when: (1) Designing real-time QEC control systems, (2) Implementing FPGA-based syndrome decoders, (3) Building low-latency feedback loops for fault-tolerant quantum computing, (4) Analyzing closed-loop latency budgets for QEC cycles, (5) Implementing mid-circuit Pauli-frame corrections in non-Clifford...businesspythongoVotes: 0GitHub stars: 3
Foveated Dynamic Token SelectionAFoveation-guided dynamic token selection for robust and efficient vision transformers. Inspired by human visual system foveated sampling + eye movements. Use when building efficient ViTs, dynamic token pruning/selection, or robustness-to-noise/adversarial without explicit robust training.businessgoVotes: 0GitHub stars: 3
Fluxonium Scalable ArchitectureAScalable fluxonium quantum processor architecture using tunable-coupler unit cells. Achieves 99.9% CZ gate fidelity and demonstrates 22-qubit GHZ state generation. Alternative to transmon-based superconducting quantum computers. Keywords: fluxonium, superconducting qubits, tunable coupler, scalable quantum processor, CZ gate, high fidelity, quantum hardware.businessgoVotes: 0GitHub stars: 3
Flexibrain Resolution Agnostic Fmri EncodingAFlexiBrain - Resolution-agnostic voxel-level encoding framework for native fMRI based on Mamba-JEPA. Bypasses destructive spatial standardization, reduces preprocessing costs, and accelerates robust voxel-level fMRI foundation models.businessgitperformanceVotes: 0GitHub stars: 3
Firing Rate Nn Mpc ImplementationAFiring rate neural network implementations of Model Predictive Control (MPC) for real-time control applications. Activation: firing rate MPC, neural network control, model predictive control, real-time neural control, rate-coded neural MPC.businesspythongoVotes: 0GitHub stars: 3
Psaas Portfolio Selection For Automated Algorithm Selection In Blackbox OptimizationA**arXiv ID:** 2310.10685 **Authors:** Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic, Ana Nikolikj, Urban Skvorc, Peter Korosec, Carola Doerr, Tome Eftimov **Published:** 2023-10-14T12:13:41Z **Abstract:** The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires balancing the trade-off between the higher flexibility of large portfolios with the increas...businessgoperformanceVotes: 0GitHub stars: 3
Modeling The Telemarketing Process Using Genetic Algorithms And Extreme Boosting Feature Selection And Costsensitive Analytical ApproachA**arXiv ID:** 2310.19843 **Authors:** Nazeeh Ghatasheh, Ismail Altaharwa, Khaled Aldebei **Published:** 2023-10-30T08:46:55Z **Abstract:** Currently, almost all direct marketing activities take place virtually rather than in person, weakening interpersonal skills at an alarming pace. Furthermore, businesses have been striving to sense and foster the tendency of their clients to accept a marketing offer. The digital transformation and the increased virtual presence forced firms to seek novel m...businessgogitVotes: 0GitHub stars: 3
Markets Hard To Predict FrameworkAMarket predictability framework distinguishing epistemic uncertainty (reducible) from aleatoric uncertainty (irreducible) in financial markets. Based on the thesis that markets are not random but hard to predict — with profound implications for investment strategy, risk management, and portfolio construction.businessgotestingVotes: 0GitHub stars: 3
Derivative Informed Operator Learning FinanceADerivative-informed operator learning framework for financial decision systems — matching pricing operators and Fréchet derivatives to reduce hedging error (Vega -40%, Delta -15%).businessgoexpressVotes: 0GitHub stars: 3
Dealer Market Competition Nash EquilibriumAVariational approach to modeling dealer market competition with internalisation and externalisation — closed-form Nash equilibrium for multi-dealer order flow competition with inventory risk management.businessgoexpressVotes: 0GitHub stars: 3
Ferroelectric Snn EegAPersonalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing. Covers deployment of SNNs on ferroelectric memristive hardware for adaptive EEG-based motor imagery decoding, mixed-precision training with device-aware updates, and subject-specific transfer learning on neuromorphic platforms. Use when working with: ferroelectric synapses, memristive SNN deployment, EEG-based BCI personalization, neuromorphic hardware constraints, mixed-precision spiking training, or ...businessgogitVotes: 0GitHub stars: 3
Fermionic Quantum ProcessorAProgrammable fermionic quantum processors with globally controlled lattices. Universal fermionic quantum processing framework for neutral atoms in optical lattices, supporting Fermi-Hubbard type models with time-dependent control over tunneling and interaction. Keywords: fermionic quantum processing, neutral atoms, optical lattice, Fermi-Hubbard model, universal quantum computation, global control, hybrid analog-digital.businessgogitVotes: 0GitHub stars: 3
Erecon Snn Nvcim HardwareAE-ReCON energy- and resource-efficient precision-configurable sparse nvCIM macro for conventional and spiking neural edge inference. Activation: nvCIM, ReRAM CIM, SNN hardware accelerator, edge-AI hardware, compute-in-memory SNN, neuromorphic hardware macrobusinessgogitVotes: 0GitHub stars: 3
Equivariant Rl Quantum Circuit SynthesisAEquivariant reinforcement learning for Clifford quantum circuit synthesis. Use when designing RL-based quantum circuit synthesis, leveraging group symmetries in quantum operations, or building equivariant architectures for quantum computing tasks.businesspythongoVotes: 0GitHub stars: 3
Equivariant QaoaAEquivariant QAOA methodology incorporating symmetry constraints into quantum approximate optimization. Uses group-theoretic structure to reduce parameter space and improve optimization efficiency for combinatorial problems with inherent symmetries. Use when solving symmetric optimization problems, reducing QAOA parameter space, or leveraging problem structure in quantum algorithms.businessgoperformanceVotes: 0GitHub stars: 3
Ensemble Engineering QuantumAEnsemble engineering methodology to overcome destructive cancellation in quantum measurements on NISQ devices. Addresses near-uniform ensemble sampling issues that render physically relevant expectation values unobservable. Activation: ensemble engineering, quantum measurement cancellation, NISQ observable estimation, destructive quantum cancellation, quantum sampling optimization.businesspythonrustVotes: 0GitHub stars: 3
Elsaa Efficient Low Rank Sparse AttentionAEfficient Low-Rank and Sparse Attention Approximation (ELSAA) methodology for training Transformers with longer contexts while preserving both sharp token-level interactions and broad contextual mixing.businessVotes: 0GitHub stars: 3
Elsa Snn Elastic InferenceAELSA — ELastic SNN Inference Architecture for efficient neuromorphic computing, featuring near-SRAM spine/token-wise dataflow pipeline, bundled AER protocol for NoC, and mini-batch spiking Gustavson-product for exploiting SNN sparsity. ISCA 2026. 3.4× speedup and 13.6× energy efficiency vs SOTA. arXiv:2605.20802businessgoapiVotes: 0GitHub stars: 3
Elastic Spiking TransformerAMatryoshka-style elasticity for Spiking Transformers - runtime-adaptive architecture enabling dynamic width and attention head slicing at inference without retraining. Applies to SNN deployment on neuromorphic hardware, edge AI, and adaptive computation. Activation: elastic spiking transformer, matryoshka spiking, runtime adaptive SNN, granularity-aware weight sharing, dynamic slicing spiking neural network.businesspythongoVotes: 0GitHub stars: 3
Elastic Spiking Transformer MatryoshkaAMatryoshka-style elastic Spiking Transformer with runtime-adaptive width and attention head slicing for deployment across hardware budgets without retraining. Reduces spike firing rates proportionally to parameter footprint. Use when deploying SNNs on constrained neuromorphic hardware, edge devices, or gesture recognition tasks. Activation: elastic spiking transformer, Matryoshka spiking network, runtime-adaptive SNN, dynamic width spiking, gesture understanding SNN, nested elasticity SNNbusinessVotes: 0GitHub stars: 3