Knowledge Transfer Network
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Related Articles from SNS
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding
arXiv:2605.30742v1 Announce Type: new Abstract: This paper addresses the task of temporal sentence grounding (TSG). Although many respectable works have made decent achievements in this important topic, they severely rely on massive expensive video-query paired annotations, which require a tremendous amount of human effort to collect in real-world applications. To this end, in this paper, we target a more practical but challenging TSG setting: unsupervised temporal sentence grounding, where...
Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
arXiv:2603.07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales. In response to this challenge, recent approaches typically resort to either parameter selection, which fails to capture the interdependent structure of this...
Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks
arXiv:2606.08978v1 Announce Type: new Abstract: Hypergraph knowledge distillation aims to retain the predictive performance of a hypergraph neural network (HNN) teacher while reducing inference costs through a lightweight student model. In this work, we observe that HNNs exhibit substantially lower prediction performance on heterophilic nodes connected through semantically diverse hyperedges, indicating that the reliability of teacher knowledge varies across nodes. Motivated by this...
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
arXiv:2507.15336v3 Announce Type: replace Abstract: Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural architectural search is computationally expensive, while model retrieval often yields suboptimal static checkpoints. To resolve this dilemma, we model the performance gains induced by fine-grained architectural modifications as edit-effect evidence and build evidence...
Balancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization
arXiv:2605.17839v3 Announce Type: replace Abstract: Knowledge distillation transfers knowledge from a high capacity teacher to a compact student using a mixture of hard and soft losses. On imbalanced data, a fixed weighting between hard and soft losses becomes brittle the learning process. Recent studies try to reweight these components in long-tailed settings.
Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
arXiv:2604.09487v2 Announce Type: replace Abstract: Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice due to inherent nonlinearities, friction, and hysteresis, which complicate modeling and control. So far, these challenges have hindered policy transfer from simulation to real systems.
Graph Machine Learning in the Era of Large Language Models (LLMs)
Announce Type: replace Abstract: Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph Machine Learning (Graph ML), facilitating the representation and processing of graphs. Recently, LLMs have demonstrated unprecedented capabilities in language tasks and are widely adopted in a variety of...
AI crosses catalyst boundaries to uncover new route for green hydrogen
AI crosses catalyst boundaries to uncover new route for green hydrogen Gaby Clark Scientific Editor Robert Egan Associate Editor Discovering new catalysts is one of the central challenges in developing clean-energy technologies such as green hydrogen production. Yet catalyst discovery has traditionally remained confined within individual material families, limiting researchers' ability to transfer knowledge across chemically distinct systems. AI unites separate catalyst families A research...
HyperPatch: Sequential Knowledge Editing Under n-ary Structural Drift
Announce Type: new Abstract: Large Language Models (LLMs) rely on Knowledge Editing (KE) to maintain temporal validity, yet real-world knowledge is inherently n-ary. We demonstrate that in non-stationary environments, sequential updates to complex relations induce N-ary Structural Drift, a phenomenon where the binary reification of n-ary events into triples fractures relational atomicity. This precipitates Structure-Conditioned Knowledge Transfer Failure, a systematic mis-grounding of the...
Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
arXiv:2606.03979v1 Announce Type: new Abstract: The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs). Despite showing promising results in tasks that require instant prediction or in-context learning, existing models lack the ability to continually learn and effectively transfer their temporal in-context knowledge to their long-term...