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Found 12 papers, 1 papers with code
Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings
In unsupervised domain adaptation, we aim to train a model that works well on a target domain when provided with labeled source samples and unlabeled target samples.
EFSA: Towards Event-Level Financial Sentiment Analysis
In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text.
Building Flexible Machine Learning Models for Scientific Computing at Scale
Foundation models have revolutionized knowledge acquisition across domains, and our study introduces OmniArch, a paradigm-shifting approach designed for building foundation models in multi-physics scientific computing.
PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization
The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features.
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems.
Learning Music Sequence Representation from Text Supervision
Music representation learning is notoriously difficult for its complex human-related concepts contained in the sequence of numerical signals.
A Method for Training-free Person Image Picture Generation
To solve this problem, the Character Image Feature Encoder model proposed in this paper enables the user to use the process by simply providing a picture of the character to make the image of the character in the generated image match the expectation.
Advancing underwater acoustic target recognition via adaptive data pruning and smoothness-inducing regularization
Underwater acoustic recognition for ship-radiated signals has high practical application value due to the ability to recognize non-line-of-sight targets.
MoEC: Mixture of Expert Clusters
Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead.
Task-Specific Expert Pruning for Sparse Mixture-of-Experts
The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity.
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e. g., search history, medical record, bank account).
Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained models
Pre-trained language models (e. g, BERT, GPT-3) have revolutionized the NLP research and fine-tuning becomes the indispensable step of downstream adaptation.
