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Found 16 papers, 8 papers with code
Biologically-Plausible Topology Improved Spiking Actor Network for Efficient Deep Reinforcement Learning
Diverging from the conventional direct linear weighted sum, the BPT-SAN models the local nonlinearities of dendritic trees within the inter-layer connections.
Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification
Energy-efficient spikformer has been proposed by integrating the biologically plausible spiking neural network (SNN) and artificial Transformer, whereby the Spiking Self-Attention (SSA) is used to achieve both higher accuracy and lower computational cost.
MM-LLMs: Recent Advances in MultiModal Large Language Models
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.
Continual Named Entity Recognition without Catastrophic Forgetting
This issue is intensified in CNER due to the consolidation of old entity types from previous steps into the non-entity type at each step, leading to what is known as the semantic shift problem of the non-entity type.
Continual Named Entity Recognition
named-entity-recognition
+1
Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition
However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i. e., old and future entity types are labeled as the non-entity type in the current task).
Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms
The results indicate that compared to the SOTA Spikformer with SSA, Spikformer with LT achieves higher Top-1 accuracy on neuromorphic datasets (i. e., CIFAR10-DVS and DVS128 Gesture) and comparable Top-1 accuracy on static datasets (i. e., CIFAR-10 and CIFAR-100).
Federated Incremental Semantic Segmentation
Moreover, new clients collecting novel classes may join in the global training of FSS, which further exacerbates catastrophic forgetting.
Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable features from a well-labeled source domain to a related unlabeled target domain, has been widely progressed.
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition
The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
Learning from the interaction is the primary way biological agents know about the environment and themselves.
HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval
It also achieves about +4. 9 AR on COCO and +3. 8 AR on Flickr30K than LightingDot and achieves comparable performance with the state-of-the-art (SOTA) fusion-based model METER.
Recent Advances and New Frontiers in Spiking Neural Networks
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware.
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance.
Ranked #3 on
Causal Emotion Entailment
on RECCON
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning
Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding.
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer
For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance.
