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Found 34 papers, 10 papers with code
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability.
Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
To facilitate EMTO's performance, various knowledge transfer models have been developed for specific optimization tasks.
PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
Our experimental results on a wide range of pattern recognition tasks demonstrate the superiority of PMSN.
Distance-Forward Learning: Enhancing the Forward-Forward Algorithm Towards High-Performance On-Chip Learning
The Forward-Forward (FF) algorithm was recently proposed as a local learning method to address the limitations of backpropagation (BP), offering biological plausibility along with memory-efficient and highly parallelized computational benefits.
Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting
Here, we take advantage of spiking neural networks' energy efficiency and propose an end-to-end lightweight KWS model.
Autonomous Multi-Objective Optimization Using Large Language Model
Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives.
Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features.
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains.
CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals the underlying data distribution.
Event-Driven Learning for Spiking Neural Networks
Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms.
Scaling Supervised Local Learning with Augmented Auxiliary Networks
AugLocal constructs each hidden layer's auxiliary network by uniformly selecting a small subset of layers from its subsequent network layers to enhance their synergy.
Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals.
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
As the first comprehensive review focused on the EA research in the era of LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs.
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
The high-dimensional algorithm representation extracted by LLM, after undergoing a feature selection module, is combined with the problem representation and passed to the similarity calculation module.
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate
Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing.
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs.
Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction
However, the effectiveness of these models is hindered in real-world scenarios due to the unreliable or even absence of pre-registered cues.
Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks
Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
Long Short-term Memory with Two-Compartment Spiking Neuron
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience.
Training Spiking Neural Networks with Local Tandem Learning
The LTL rule follows the teacher-student learning approach by mimicking the intermediate feature representations of a pre-trained ANN.
Target Speaker Verification with Selective Auditory Attention for Single and Multi-talker Speech
Inspired by the study on target speaker extraction, e. g., SpEx, we propose a unified speaker verification framework for both single- and multi-talker speech, that is able to pay selective auditory attention to the target speaker.
Deep Convolutional Spiking Neural Networks for Keyword Spotting
To explore the effectiveness and computational complexity of SNN on KWS and wakeword detection, we compare the performance and computational costs of spiking fully-connected and convolutional neural networks with ANN counterparts under clean and noisy testing conditions.
Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network
Inspired by the mammal's auditory localization pathway, in this paper we propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with a microphone array.
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.
You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy
Rate-encoded SNNs could be seen as inefficient as an encoding scheme because it involves the transmission of a large number of spikes.
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner.
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Neural Population Coding for Effective Temporal Classification
We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM).
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks
The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve.
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.
Deep Spiking Neural Network with Spike Count based Learning Rule
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart.
