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- PMR: Prototypical Modal Rebalance for Multimodal Learning249
Prototypical Modality Rebalance (PMR) is proposed to perform stimulation on the particular slow-learning modality without interference from other modalities, and only relies on the representations of each modality and without restrictions from model structures and fusion methods, making it with great application potential for various scenarios.
- Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models118
This work develops the Context and Text-aware Token Selection (CT2S) strategy, which preserves only unimportant vision tokens after early layers of LVLMs to adaptively amplify text-informed hallucination during the auto-regressive decoding.
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- Towards Efficient Replay in Federated Incremental Learning59
This work proposes to employ a simple, generic frame-work for FIL named Re-Fed, which can coordinate each client to cache important samples for replay, alleviating the catastrophic forgetting problem in Federated Incremental Learning scenarios where edge clients may lack enough storage space to retain full data.
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- Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey53
A large-scale benchmark is established to revisit this problem and analyze the performance of the state-of-the-art NCCL approaches and the important challenges and future research directions in NCCL are discussed.
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- FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers47
It is demonstrated that the fine-tuned Transformers achieve extraordinary performance on FL, and that the lightweight fine- Tuning method facilitates a fast convergence rate and low communication costs.
- FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices45
This paper identifies that when making predictions, different clients tend to activate different neurons of the entire model related to their respective distributions, and proposes a novel method called FedDSE, which can reduce the conflicts among clients by extracting sub-models based on the data distribution of each client.
- Aligning Language Models with Human Preferences via a Bayesian Approach42
A novel approach is proposed, which employs a Bayesian framework to account for the distribution of disagreements among human preferences as training a preference model, and names it as d-PM, which consistently exceeds previous SOTA models in both automatic and human evaluations.
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- FedNLR: Federated Learning with Neuron-wise Learning Rates40
This paper proposes a novel and simple algorithm called FedNLR, which utilizes Neuron-wise Learning Rates during the FL local training process to enhance the learning of neurons bound to local classes on local data knowledge while reducing the decay of non-local classes knowledge stored in neurons.
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- Edge Learning38
A comprehensive survey of the recent research efforts on Edge Learning, which provides an overview of the enabling technologies for EL, including model training, inference, security guarantee, privacy protection, and incentive mechanism.
- Personalized Federated Recommendation for Cold-Start Users via Adaptive Knowledge Fusion35
This work takes a dive into the federated recommendation for cold-start users, a novel scenario where the new participating users can directly obtain a promising recommendation without comprehensive training with all participating users by leveraging both transferred knowledge from the converged warm clients and the knowledge learned from the local data.
- FR: Folded Rationalization with a Unified Encoder33
Folded Rationalization (FR) is proposed that folds the two phases of the rationale model into one from the perspective of text semantic extraction to facilitate a better predictor by access to valuable information blocked by the generator in the traditional two-phase model and thus bring a better generator.
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- Non-exemplar Online Class-Incremental Continual Learning via Dual-Prototype Self-Augment and Refinement29
A novel Dual-prototype Self-augment and Refinement method (DSR) for NO-CL problem, which consists of two strategies: Dual class prototypes: vanilla and high-dimensional prototypes are exploited to utilize the pre-trained information and obtain robust quasi-orthogonal representations rather than example buffers for both privacy preservation and memory reduction.
- D-Separation for Causal Self-Explanation29
This study proposes a novel criterion to uncover the causal rationale, termed the Minimum Conditional Dependence (MCD) criterion, and employs a simple and practical measure of dependence, specifically the KL-divergence, to validate the proposed MCD criterion.
- DCSpell: A Detector-Corrector Framework for Chinese Spelling Error Correction28
A cloze-style detector-corrector framework (DCSpell) that firstly detects whether a character is erroneous before correcting it, and employs sentence pairs as the input, which potentially incorporates the knowledge of phonological and visual similarity.
- OSP28
A new synchronization parallel mechanism named overlap synchronization parallel (OSP), in which the waiting time is removed by conducting computation and communication in an overlapped manner, and it is theoretically proved that this mechanism could achieve the same convergence rate compared to the sequential SGD for non-convex problems.
- Heterogeneity-aware Gradient Coding for Straggler Tolerance26
A novel heterogeneity-aware gradient coding scheme which can not only tolerate a predetermined number of stragglers but also fully utilize the computation capabilities of heterogenous workers is proposed.
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 2026-10-12. 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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