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Found 25 papers, 14 papers with code
AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage
Building on this idea, we propose AutoReproduce, a multi-agent framework capable of automatically reproducing experiments described in research papers in an end-to-end manner.
LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners
Lifelong learning is essential for intelligent agents operating in dynamic environments.
Safety in Large Reasoning Models: A Survey
Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities.
From System 1 to System 2: A Survey of Reasoning Large Language Models
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning.
Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs
These include a pre-training task focused on mastering external linguistic structures and knowledge, as well as three fine-tuning tasks: multiple-choice question answering, generative question answering, and style transfer, each aligning the LLM with Lu Xun's internal ideation and writing style.
Lifelong Learning of Large Language Model based Agents: A Roadmap
This survey is the first to systematically summarize the potential techniques for incorporating lifelong learning into LLM-based agents.
Federated Incremental Named Entity Recognition
In a more realistic scenario, local clients receive new entity types continuously, while new local clients collecting novel data may irregularly join the global FNER training.
How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?
Moreover, they heavily suffer from catastrophic forgetting and concept neglect on old personalized concepts when continually learning a series of new concepts.
Prompt-guided Precise Audio Editing with Diffusion Models
Audio editing involves the arbitrary manipulation of audio content through precise control.
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 interaction is the primary way that biological agents acquire knowledge about their 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 #4 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.
