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- A Survey on Evaluation of Large Language Models3,896
This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate, and offers invaluable insights to researchers in the realm of LLMs evaluation.
- Deep learning for sensor-based activity recognition: A survey1,998
The recent advance of deep learning based sensor-based activity recognition is surveyed from three aspects: sensor modality, deep model, and application and detailed insights on existing work are presented and grand challenges for future research are proposed.
- Generalizing to Unseen Domains: A Survey on Domain Generalization1,906
This paper provides a formal definition of domain generalization and discusses several related fields, and categorizes recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and presents several popular algorithms in detail for each category.
- Deep Subdomain Adaptation Network for Image Classification1,245
This work presents a deep subdomain adaptation network (DSAN) that learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD).
- FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare999
FedHealth is proposed, the first federated transfer learning framework for wearable healthcare that performs data aggregation through federated learning, and then builds relatively personalized models by transfer learning.
- Visual Domain Adaptation with Manifold Embedded Distribution Alignment657
This paper proposes a Manifold Embedded Distribution Alignment (MEDA) approach, which learns a domain-invariant classifier in Grassmann manifold with structural risk minimization, while performing dynamic distribution alignment to quantitatively account for the relative importance of marginal and conditional distributions.
- Balanced Distribution Adaptation for Transfer Learning588
This paper proposes a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA.
- FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning521
This work proposes FreeMatch to adjust the confidence threshold in a self-adaptive manner according to the model's learning status and introduces aSelf- Adaptive class fairness regularization penalty to encourage the model for diverse predictions during the early training stage.
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- Transfer Learning with Dynamic Adversarial Adaptation Network401
This paper proposes a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions.
- AdaRNN389
This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data and proposes Temporal Distribution Characterization to better characterize the distribution information in the TS.
- Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals368
A novel deep learning-based anomaly detection algorithm called CAE-M, which builds a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) and a Memory Network consisting of linear and non-linear predictions to capture temporal dependence from time-series data.
- PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts351
This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic to demonstrate that contemporary LLMs are not robust to adversarial prompts.
- On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective323
A thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective is conducted and it is found that it tends to provide informal suggestions for medical tasks instead of definitive answers.
- Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity305
This survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs, focusing two primary LLM configurations standalone LLMs and Retrieval-Augmented LLMs that utilizes external data.
- SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning291
This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation and demonstrates the inherent quantity-quality trade-off problem of pseudo-labels with thresholding, which may prohibit learning.
- Multi-representation adaptation network for cross-domain image classification286
Multi-Representation Adaptation Network (MRAN) is presented which can dramatically improve the classification accuracy for cross-domain image classification and specially aims to align the distributions of multiple representations extracted by a hybrid structure named Inception Adaptation Module (IAM).
- Stratified Transfer Learning for Cross-domain Activity Recognition252
The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition and extensively investigate the performance of STL across different degrees of similarities and activity levels between domains.
- Easy Transfer Learning By Exploiting Intra-Domain Structures168
This paper proposes a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance and satisfies the Occam's Razor principle.
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- Domain-invariant Feature Exploration for Domain Generalization149
It is argued that domain-invariant features should be originating from both internal and mutual sides, and DIFEX for Domain-Invariant Feature EXploration is proposed, which achieves state-of-the-art performance.
- Deep Transfer Learning for Cross-domain Activity Recognition148
An effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR) that is able to select the most similar K source domains from a list of available domains is proposed and an effective Transfer Neural Network to perform knowledge transfer for Activity recognition (TNNAR).
- Memory-Guided Multi-View Multi-Domain Fake News Detection143
A Domain Memory Bank is proposed to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics, and a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains.
- Adaptive Memory Networks With Self-Supervised Learning for Unsupervised Anomaly Detection129
A novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) is proposed to address challenges and enhance the generalization ability in unsupervised anomaly detection and is significantly improves the performance compared to other state-of-the-art methods.
- MetaFed: Federated Learning Among Federations With Cyclic Knowledge Distillation for Personalized Healthcare117
A novel framework called MetaFed is proposed to facilitate trustworthy FL between different federations, which obtains a personalized model for each federation without a central server via the proposed cyclic knowledge distillation.
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- Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets113
Conv-Adapter, a PET module designed for ConvNets, is light-weight, domain-transferable, and architecture-agnostic with generalized performance on different tasks with comparable or surpasses the performance of full fine-tuning on 23 classification tasks of various domains.
- Personalized Federated Learning With Adaptive Batchnorm for Healthcare102
FedAP is proposed to tackle domain shifts and then obtain personalized models for local clients by learning the similarity between clients based on the statistics of the batch normalization layers while preserving the specificity of each client with different local batch normalization.
- MixSpeech: Data Augmentation for Low-Resource Automatic Speech Recognition93
Experimental results show that MixSpeech achieves better accuracy than the baseline models without data augmentation, and outperforms a strongData augmentation method SpecAugment on these recognition tasks.
- Cross-Dataset Activity Recognition via Adaptive Spatial-Temporal Transfer Learning87
An Adaptive Spatial-Temporal Transfer Learning (ASTTL) approach to tackle both of the above two challenges in cross-dataset HAR and can be used for both source domain selection and accurate activity transfer.
- Exploiting Adapters for Cross-Lingual Low-Resource Speech Recognition83
This paper proposes a novel algorithm called SimAdapter for explicitly learning knowledge from adapters for parameter-efficient cross-lingual speech adaptation and shows that these two novel algorithms can be integrated for better performance with up to 3.55% relative WER reduction.
- Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition78
This paper introduces semantic-aware Mixup that considers the activity semantic ranges to overcome the semantic inconsistency brought by domain differences and introduces the large margin loss to enhance the discrimination of Mixup to prevent misclassification brought by noisy virtual labels.
- Liquefaction of kraft lignin by hydrocracking with simultaneous use of a novel dual acid-base catalyst and a hydrogenation catalyst76
It is demonstrated that the combination of the two catalysts is an efficient catalyst for liquefaction of lignin, with little char formation, which has the potential to produce valuable chemicals and fuels from lign in under moderate conditions.
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- Exploring Vision-Language Models for Imbalanced Learning68
Improved VLMs significantly outperforms zero-shot classification by an average accuracy of 6.58%, 69.82%, and 6.17%, on ImageNet-LT, iNaturalist18, and Places-LT, respectively, and the significance of imbalanced learning algorithms in face of VLMs pre-trained by huge data is highlighted.
- Cross-domain activity recognition via substructural optimal transport68
This paper proposes substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer and proposes an optimal transport-based implementation, Substructural Optimal Transport (SOT), for cross-domain HAR.
- Domain Generalization for Activity Recognition via Adaptive Feature Fusion66
This article proposes Adaptive Feature Fusion for Activity Recognition (AFFAR), a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model’s generalization performance.
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- Large Language Models Understand and Can be Enhanced by Emotional Stimuli57
Emo-tionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for interaction between humans and LLMs and was observed to improve both the truthfulness and informativeness.
- Diversify: A General Framework for Time Series Out-of-Distribution Detection and Generalization56
Qualitative and quantitative results demonstrate that Diversify learns more generalized features and significantly outperforms other baselines, as well as theoretical insights support the framework's validity.
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- Survey on Factuality in Large Language Models50
This survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs, and examines the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors.
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- Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning43
Robustness Critical Fine-Tuning (RiFT), a novel approach to enhance generalization without compromising adversarial robustness, is proposed and experiments show that RiFT can significantly improve both generalization and out-of-distribution robustness.
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- How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation30
This study rigorously evaluates GPT-4V's adaptability and generalization capabilities in dynamic environments, benchmarking against prominent models like CLIP, LLaVA, and Gemini and delves into GPT-4V's zero-shot generalization across 13 diverse datasets spanning natural, medical, and molecular domains.
- Supervised Knowledge Makes Large Language Models Better In-context Learners30
Using the proposed plug-in method, enhanced versions of Llama 2 and ChatGPT surpass their original versions regarding generalizability and factuality and the potential of the methodology in fostering more reliable LLMs is highlighted.
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- Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future28
This paper presents the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding, and hopes this survey will encourage future research in this area.
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- DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System26
This work proves that directly aligning the representations of LLMs and collaborative models is suboptimal for enhancing downstream recommendation tasks performance, based on the information theorem, and proposes a novel plug-and-play alignment framework for LLMs and collaborative models.
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- FIXED: Frustratingly Easy Domain Generalization with Mixup21
This work proposes a simple yet effective enhancement for Mixup-based DG, namely domain-invariant Feature mIXup (FIX), which learns domain- Invariant representations for Mix up and significantly outperforms nine state-of-the-art related methods.
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- Trustworthy Machine Learning: Robustness, Generalization, and Interpretability16
This tutorial will give a comprehensive introduction to the recent advance of trustworthy machine learning in robustness, generalization, and interpretability.
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- Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction4
This paper proposes a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity.
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- Out-of-Distribution Generalization in Text Classification: Past, Present, and Future3
This paper presents the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in text classification, and discusses the challenges involved and potential future research directions.
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- A Tutorial on Domain Generalization1
This tutorial presents the recent advance of domain generalization and introduces the background, formulation, and theory behind this topic, with a primary focus on the methodology, evaluation, and applications.
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- Study of Active Learning Support Vector Machine and Its Application on Mongolian Text Classification1
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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