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Task-Agnostic Decision Transformer for Multi-Type Agent Control with Federated Split Training
With the rapid advancements in artificial intelligence, the development of knowledgeable and personalized agents has become …
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Task-Agnostic Decision Transformer for Multi-Type Agent Control with Federated Split Training
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing …
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From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Medical Speech Symptoms Classification via Disentangled Representation
Intent is defined for understanding spoken language in existing works. Both textual features and acoustic features involved in medical …
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Medical Speech Symptoms Classification via Disentangled Representation
Gecko: Resource-Efficient and Accurate Queries in Real-Time Video Streams at the Edge
Surveillance cameras are ubiquitous nowadays and users’ increasing needs for accessing real-world information (e.g., finding abandoned …
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Gecko: Resource-Efficient and Accurate Queries in Real-Time Video Streams at the Edge
EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model
In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating …
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EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model
ED-TTS: Multi-Scale Emotion Modeling Using Cross-Domain Emotion Diarization for Emotional Speech Synthesis
Existing emotional speech synthesis methods often utilize an utterance-level style embedding extracted from reference audio, neglecting …
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ED-TTS: Multi-Scale Emotion Modeling Using Cross-Domain Emotion Diarization for Emotional Speech Synthesis
Learning Disentangled Speech Representations with Contrastive Learning and Time-Invariant Retrieval
Voice conversion refers to transferring speaker identity with well-preserved content. Better disentanglement of speech representations …
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Learning Disentangled Speech Representations with Contrastive Learning and Time-Invariant Retrieval
INCPrompt: Task-Aware Incremental Prompting for Rehearsal-Free Class-incremental Learning
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. …
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INCPrompt: Task-Aware Incremental Prompting for Rehearsal-Free Class-incremental Learning
Leveraging Biases in Large Language Models: bias-kNN for Effective Few-Shot Learning
Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning. …
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Leveraging Biases in Large Language Models: bias-kNN for Effective Few-Shot Learning
P2DT: Mitigating Forgetting in Task-Incremental Learning with Progressive Prompt Decision Transformer
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance …
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P2DT: Mitigating Forgetting in Task-Incremental Learning with Progressive Prompt Decision Transformer