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- Arxiv 2605 00025 Modal Self Supervised Neural Modality Discovery ViMoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis (arXiv: 2605.00025)Votes: 0GitHub stars: 3
- Anomaly Localization In Model Gradients Under Backdoor Attacks Against Federated Learning**arXiv ID:** 2111.14683 **Authors:** Zeki Bilgin **Published:** 2021-11-29T16:46:01Z **Abstract:** Inserting a backdoor into the joint model in federated learning (FL) is a recent threat raising concerns. Existing studies mostly focus on developing effective countermeasures against this threat, assuming that backdoored local models, if any, somehow reveal themselves by anomalies in their gradients. However, this assumption needs to be elaborated by identifying specifically which gradients ar...Votes: 0GitHub stars: 3
- An Effective Algorithm For Hyperparameter Optimization Of Neural Networks**arXiv ID:** 1705.08520 **Authors:** Gonzalo Diaz, Achille Fokoue, Giacomo Nannicini, Horst Samulowitz **Published:** 2017-05-23T20:17:44Z **Abstract:** A major challenge in designing neural network (NN) systems is to determine the best structure and parameters for the network given the data for the machine learning problem at hand. Examples of parameters are the number of layers and nodes, the learning rates, and the dropout rates. Typically, these parameters are chosen based on heuristic r...Votes: 0GitHub stars: 3
- Agilenet Lightweight Dictionarybased Fewshot Learning**arXiv ID:** 1805.08311 **Authors:** Mohammad Ghasemzadeh, Fang Lin, Bita Darvish Rouhani, Farinaz Koushanfar, Ke Huang **Published:** 2018-05-21T22:36:11Z **Abstract:** The success of deep learning models is heavily tied to the use of massive amount of labeled data and excessively long training time. With the emergence of intelligent edge applications that use these models, the critical challenge is to obtain the same inference capability on a resource-constrained device while providing ada...Votes: 0GitHub stars: 3
- A Layer Wise Interactive Dual Stream Network For EElectroencephalography (EEG) provides a non-invasive window into brain activity, offering high temporal resolution crucial for understanding and interacting with neural processes through brain-compute...Votes: 0GitHub stars: 3
- A Generalized Framework For Population Based Training**arXiv ID:** 1902.01894 **Authors:** Ang Li, Aleksandra Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, Pramod Gupta **Published:** 2019-02-05T20:11:17Z **Abstract:** Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training. Previous PBT implementations have been synchronized glass-box sys...Votes: 0GitHub stars: 3
- Game Energetic Ei NetworksGame-theoretic energetic framework for excitatory-inhibitory neural circuits with asymmetric connectivity and stability analysis.Votes: 0GitHub stars: 3
- Funessian Process Non MarkovianFunessian过程：一种连续时间正可分非马尔可夫过程，具有初始状态记忆。平稳态下关联函数指数衰减（通常被视为马尔可夫特征），但记忆贯穿演化。互信息作为非马尔可夫性度量。应用于随机游走，展示记忆效应打破遍历性并改变扩散系数。Votes: 0GitHub stars: 3
- Functional Whole Brain Models FwbmFunctional Whole-Brain Models (fWBMs) — unified framework integrating structural/dynamical realism with functional competenceVotes: 0GitHub stars: 3
- Functional Proximity Law MultilayerFunctional Proximity Law in Multilayer Networks: Hub importance scores persist more strongly between functionally similar layers. Validated across 17 pre-registered experiments including neuroscience (r=0.777 in C. elegans connectome). Activation: multilayer networks, functional proximity, hub importance, network layers, cross-layer similarity.Votes: 0GitHub stars: 3
- Functional Connectome Fingerprint功能性连接组指纹分析方法论。扩展 differential identifiability 框架， 检测个体指纹梯度和双胞胎指纹梯度。 触发词：脑指纹、连接组指纹、个体差异、可识别性、fingerprint、 differential identifiability, connectome fingerprint。Votes: 0GitHub stars: 3
- Functional Connectivity Graph Neural NetworksFunctional Connectivity Graph Neural Networks methodology combining structural and functional connectivity with persistent graph homology for brain-inspired graph classification. Activation triggers: functional connectivity, graph neural network, persistent homology, brain network, multi-modal GNN.Votes: 0GitHub stars: 3
- Ftqc Encoding Circuit SynthesisEncoding circuit synthesis methodology for fault-tolerant quantum computation. Constructs optimized circuits that map arbitrary logical states into error-correcting codes, minimizing two-qubit gate count and circuit depth. Use when: (1) designing fault-tolerant state preparation circuits, (2) encoding logical qubits into QECCs, (3) optimizing encoding circuit overhead, (4) compiling general-state preparation for FTQC.Votes: 0GitHub stars: 3
- Ft Primitive BenchFTPrimitiveBench methodology for fault-tolerant quantum computing benchmarking. Provides systematic approach for evaluating QEC protocols under hardware-motivated noise models including Pauli bias, measurement bias, and spatio-temporal non-uniformity. Use when: (1) analyzing fault-tolerant quantum computing performance, (2) benchmarking QEC codes under realistic noise, (3) comparing decoders for surface code, (4) studying logical primitive operations (memory, lattice surgery, Hadamard, phase ...Votes: 0GitHub stars: 3
- Frequency Matching Snn MmwaveFrequency-matching methodology for Spiking Neural Networks in mmWave sensing. LIF dynamics provide inherent low-pass filtering that suppresses high-frequency noise in mmWave signals. Derives principled criterion for membrane decay factor by matching LIF effective bandwidth to data's discriminative spectral content. Use when applying SNNs to sensor data with frequency structure, configuring SNN temporal filtering, or optimizing edge perception systems. Trigger: mmWave SNN, frequency matching L...Votes: 0GitHub stars: 3
- Fped Moe Brain DecodingFunctional-Network Prior-Guided Mixture-of-Experts (MoE) framework for interpretable brain decoding from fMRI. Uses brain network topology as expert priors with adaptive routing for visual semantic reconstruction.Votes: 0GitHub stars: 3
- Fourier Lcu Quantum OptimizationFourier-based Linear Combination of Unitaries (LCU) methodology for efficient quantum circuit decomposition in optimization algorithms. Covers ancilla-free LCU constructions, Fourier decomposition of diagonal/non-diagonal unitaries, formal connection to Lagrangian relaxation, and hardware-friendly gate layer simplification. Activation: LCU, linear combination of unitaries, Fourier quantum, quantum optimization decomposition, constraint penalty, XY-mixer, cardinality constraint, ancilla-free q...Votes: 0GitHub stars: 3
- Formalizing Binding ProblemInformation-theoretic formalization of the binding problem and probing method for measuring binding information in Vision Transformers and neural representations.Votes: 0GitHub stars: 3
- Fly Goal Normalization Fc2Analysis of Drosophila FC2 circuit mechanism showing that goal maintenance uses normalization rather than winner-take-all selection, with global inhibition from FB5A neurons keeping a single clean activity bump rather than actively choosing between competing goals.Votes: 0GitHub stars: 3
- Flow Matching Brain DynamicsFlow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics — compositional conditional generation of neural time series using continuous normalizing flows, enabling zero-shot generalization to novel experimental conditionsVotes: 0GitHub stars: 3
- Trading Inference Time Adversarial RobustnessMethodology for trading inference-time compute to improve adversarial robustness in LLMs through repeated sampling and output filtering.Votes: 0GitHub stars: 3
- Portfolio Selection Belle Art EconomicsPortfolio Selection is More of a Belle Art Than Economics or FinanceVotes: 0GitHub stars: 3
- Portfolio Optimization Mean Variance SpectrumPortfolio Optimization with Mean-Variance-Spectrum PreferencesVotes: 0GitHub stars: 3
- Market Informedness Rl Market MakingMarket making with heterogeneous agents and reinforcement learning — MAPPO algorithm with finite-horizon stability guarantees for Hawkes market-taker processes. Shows profitability increases with market informedness.Votes: 0GitHub stars: 3