TodayNewsResearchFieldsStacksMarketAnalytics
⌘K

Research

New papers ranked by attention, with their code, models and discussion linked. Pick a field on the left.

Field

Papers

Open problems

Rising research topics

Coming with more sources: more tagged papers
  • Federated learning52+40.5%
  • Agents566+9.5%
  • Frontier LLMs485+0.8%
  • Forecasting time87−4.4%
  • Inference & serving291−5.5%
  • Sensing communication15−6.2%
  • 3D scene174−6.5%
  • Image segmentation192−9.0%

Ask the research

Sample
ByteTrail · AI and developer-tools intelligence
NewsResearchStacksAPI
Fields · this week
All fields3795Multimodal598Agents567Frontier LLMs485Inference & serving291Image segmentation192Graph learning183Learning verifiable rewards1763D scene174Evals108Voice AI106Classifier & causal discovery90Flow matching87Forecasting time87Training & fine-tuning64Teacher policy distillation59Efficient spiking neural4All 32 fields
WeekMonthQuarterAll
NewestMost relevant

Papers

Papers

  • arXiv cs.LGOct 20

    Model- and Layer-Level Closed-Loop Forecasting with SSM-Based Spiking Neural Networks

    Erik Stolt, Sanja Karilanova, Ayca Ozcelikkale

    Deep State-Space Models (Deep SSMs) have shown promising performance and computational benefits in processing of time-series data with complex temporal dependencies. Spiking neural networks (SNNs) constitute a biologically inspired class of deep SSMs, where computing blocks communicate through spikes, discrete single-bit events rather than continuous activation functions. A particularly important task in time-series processing is forecasting, i.e. predicting future values of a sequence from past observations alone. However, how to best perform forecasting with SNNs is an open question, where the standard autoregressive nature of the data processing with SNNs requires values that are not available during the forecasting phase. In this article, we address this challenge. We explore two closed-loop forecasting strategies, in which the network's own generated values are fed back into the network as if they were observed past values: (i) model-level and (ii) layer-level closed-loop forecasting. We evaluate these approaches on the ETT-small datasets, a standard benchmark for time-series forecasting, under both spike-based and continuous activation functions. We compare the proposed…

    PDF
  • arXiv cs.AIOct 20

    Robust Representation Learning for Spiking Neural Networks based on Conditional Information Bottleneck

    Yuli Zha, Wang Fan, Yixiao Dai, Lingjie Wang, Bang Hu +3

    Spiking Neural Networks (SNNs) have shown great potential for low-power neuromorphic computing due to their event-driven processing and spatiotemporal sparsity, but they remain vulnerable to input perturbations, hardware-intrinsic noise, and adversarial attacks in real-world deployment. We attribute this vulnerability partly to input-specific hidden representations learned by standard SNNs trained with conventional cross-entropy loss, as our information-plane analysis empirically shows that substantial input-related information can still be retained after label-predictive information becomes stable. To address this issue, we propose SCIB, a simple yet effective robust learning objective for SNNs based on the Conditional Information Bottleneck. Different from conventional variational IB methods, SCIB replaces the mutual information compression term $I(X;S)$ with the conditional mutual information $I(X;S|Y)$ and derives a tractable variational upper bound. Moreover, it introduces a learnable class-conditional prior instead of a label-agnostic isotropic Gaussian prior, improving the expressiveness of variational compression and encouraging class-structured representations when…

    PDF
  • arXiv cs.NEOct 16

    SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks

    Kai Sun, Peibo Duan, Zhanglu Yan, Guowei Zhang, Yongsheng Huang +1

    On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synaptic weights and membrane potentials. Existing post-training quantization (PTQ) methods provide them without retraining and, like those for ANNs, minimize numerical errors such as the mean squared error (MSE). In an SNN, however, a quantization error reaches later layers only through the spikes it flips. We find that errors of equal MSE can cause very different accuracy losses, whereas errors that flip the same fraction of spikes cause the same loss. We therefore propose SpikeQ, a spike-guided PTQ framework. For weights, we select the level of each weight by simulating the downstream neurons and minimizing their spike errors. For membrane potentials, most existing methods quantize the state after the reset, whereas neuromorphic chips compare the threshold with the membrane potential stored at a fixed bit width. We target this pre-fire quantization and build a reset-aware grid that places the threshold on a rounding boundary, so rounding never flips the spike at the current time step. We evaluate SpikeQ on…

    PDF
  • arXiv cs.CVOct 15

    The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks

    Ruichen Ma, Yicai Chen, Pujun Zhou, Yue Zuo, Chongyang Li +3

    Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity. However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neurons. Quantitative analysis reveals that up to 44% of neurons in an ImageNet-trained ResNet-18 model reside in a deeply inhibited state, imposing a persistent computational burden despite having a negligible probability of firing. To address this inefficiency, the Dormant Spiking Neuron (DSN) model is introduced. By incorporating a state-driven dynamic gate, the DSN allows a neuron to enter a dormant state when its membrane potential falls below a predefined negative threshold, dynamically pruning redundant computations at the instance level. Crucially, this dormancy mechanism is directly integrated into the neuronal dynamics and trained end-to-end via backpropagation through time using surrogate gradients, enabling the network to actively learn a robust dormancy policy that adaptively preserves critical information flow. Extensive experiments across static and…

    AI30PDF
Research · AI field

Efficient spiking neural

Found by the topic model: efficient spiking neural.

Field page: people, tools, topics →
4papers this week−69.2% vs last week37 this month

Open problems in Efficient spiking neural

  • Efficient quantization for spiking neural networks1 paper raises it · 1 addresses it

    Low-bit weights and membrane potentials are needed for neuromorphic hardware but existing PTQ methods ignore spike-specific errors.

    • SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networksraises · addresses
  • Redundant subthreshold computation in SNNs1 paper raises it · 1 addresses it

    Postsynaptic neurons in subthreshold states waste computation despite low firing probability.

    • The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networksraises · addresses
  • Catastrophic forgetting in continual learning for SNNs1 paper raises it · 1 addresses it

    SNNs lose prior knowledge when learning new tasks sequentially under memory constraints.

    • Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networksraises · addresses
  • Hardware-aware training and inference for SNNs1 paper raises it · 2 address it

    Efficient on-device SNN deployment requires specialized hardware and memory-aware training.

    • An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classificationraises · addresses
    • Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networksaddresses
  • Accuracy gap in spiking language models1 paper raises it · 1 addresses it

    Spiking language models suffer a persistent loss gap compared to dense baselines, limiting their practical use.

    • Compute-Optimal Scaling Laws for Spiking Language Modelsraises · addresses

Named by an AI from these papers' abstracts.