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Found 41 papers, 16 papers with code
Deep Weighted MaxSAT for Aspect-based Opinion Extraction
Though deep learning has achieved significant success in various NLP tasks, most deep learning models lack the capability of encoding explicit domain knowledge to model complex causal relationships among different types of variables.
Variational Deep Logic Network for Joint Inference of Entities and Relations
Various deep neural networks have been proposed to jointly perform entity extraction and relation prediction, which only propagate information implicitly via representation learning.
KVTuner: Sensitivity-Aware Layer-wise Mixed Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference
KV cache quantization can improve Large Language Models (LLMs) inference throughput and latency in long contexts and large batch-size scenarios while preserving LLMs effectiveness.
CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference
Large language models (LLMs) achieve impressive performance by scaling model parameters, but this comes with significant inference overhead.
FuseGPT: Learnable Layers Fusion of Generative Pre-trained Transformers
In this paper, we propose FuseGPT, a novel methodology to recycle the pruned transformer blocks to further recover the model performance.
State Chrono Representation for Enhancing Generalization in Reinforcement Learning
SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning.
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners
Deploying a safe mobile robot policy in scenarios with human pedestrians is challenging due to their unpredictable movements.
Large Language Models Know What Makes Exemplary Contexts
In-context learning (ICL) has proven to be a significant capability with the advancement of Large Language models (LLMs).
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning
However, ICL exhibits significant efficacy in regulating the label space and format, which helps LLMs respond to desired label words.
Whispers in Grammars: Injecting Covert Backdoors to Compromise Dense Retrieval Systems
This paper investigates a novel attack scenario where the attackers aim to mislead the retrieval system into retrieving the attacker-specified contents.
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning
Large language models (LLMs) have showcased their capability with few-shot inference known as in-context learning.
SOUL: Towards Sentiment and Opinion Understanding of Language
These findings underscore the challenging nature of the SOUL task for existing models, emphasizing the need for further advancements in sentiment analysis to address its complexities.
Multilingual Jailbreak Challenges in Large Language Models
The experimental results reveal that in the unintentional scenario, the rate of unsafe content increases as the availability of languages decreases.
Sentiment Analysis in the Era of Large Language Models: A Reality Check
This paper aims to provide a comprehensive investigation into the capabilities of LLMs in performing various sentiment analysis tasks, from conventional sentiment classification to aspect-based sentiment analysis and multifaceted analysis of subjective texts.
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis
Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Deep Multitask Learning with Progressive Parameter Sharing
A scheduling mechanism following the concept of curriculum learning is also designed to progressively change the sharing strategy to increase the level of sharing during the learning process.
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem.
Fast Graph Generation via Spectral Diffusion
In this paper, we argue that running full-rank diffusion SDEs on the whole graph adjacency matrix space hinders diffusion models from learning graph topology generation, and hence significantly deteriorates the quality of generated graph data.
Learning Gradient-based Mixup towards Flatter Minima for Domain Generalization
To address the distribution shifts between training and test data, domain generalization (DG) leverages multiple source domains to learn a model that generalizes well to unseen domains.
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation
In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach.
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Training on existing data often makes the model biased towards the distribution of the training data, thus the model might perform terribly on test data with different distributions.
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily.
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions
Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs).
Reinforcement Learning with Efficient Active Feature Acquisition
Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem.
Integrating Deep Learning with Logic Fusion for Information Extraction
To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner.
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization
However, these methods only heuristically make training-based quantization applicable, without further analysis on how the approximated gradients can assist training of a quantized network.
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction
In this article, we explore the constructions of recursive neural networks based on the dependency tree of each sentence for associating syntactic structure with feature learning.
Transfer Value Iteration Networks
However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained.
Sequence-level Intrinsic Exploration Model for Partially Observable Domains
First, we propose a new reasoning paradigm to infer the novelty for the partially observable states, which is built upon forward dynamics prediction.
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction
Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization.
Domain Generalization With Adversarial Feature Learning
In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an âunseenâ target domain by taking the advantage of multiple seen source-domain data.
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Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
In deep reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return.
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems.
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms
To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence.
Multi-task memory networks for category-specific aspect and opinion terms co-extraction
This problem involves the identification of aspect and opinion terms within each sentence, as well as the categorization of the identified terms.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Distributed Multi-Task Relationship Learning
Due to heavy communication caused by transmitting the data and the issue of data privacy and security, it is impossible to send data of different task to a master machine to perform multi-task learning.
Transfer Hashing with Privileged Information
Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+.
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
