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Found 15 papers, 9 papers with code
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models
Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences.
A Debiased Nearest Neighbors Framework for Multi-Label Text Classification
For confidence estimation bias, we present a debiased confidence estimation strategy, improving the adaptive combination of predictions from $k$NN and inductive binary classifications.
Multi-Prompting Decoder Helps Better Language Understanding
Despite the effectiveness of these methods, they only use a single prompt to query PLMs for decoding, leading to a heavy reliance on the quality of the adopted prompt.
V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation
In our work on portrait video generation, we identified audio signals as particularly weak, often overshadowed by stronger signals such as facial pose and reference image.
Ensembling Diffusion Models via Adaptive Feature Aggregation
In this paper, we propose a novel ensembling method, Adaptive Feature Aggregation (AFA), which dynamically adjusts the contributions of multiple models at the feature level according to various states (i. e., prompts, initial noises, denoising steps, and spatial locations), thereby keeping the advantages of multiple diffusion models, while suppressing their disadvantages.
MCA: Moment Channel Attention Networks
In this paper, we investigate the statistical moments of feature maps within a neural network.
SignGraph: A Sign Sequence is Worth Graphs of Nodes
In fact sign language tasks need to focus on the correlation of different regions in one frame and the interaction of different regions among adjacent frames for identifying a sign sequence.
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e. g., coming up with the right arguments for calling routines), requiring a deeper comprehension of complex file interactions.
Reconfigurable Intelligent Surface & Edge -- An Introduction of an EM manipulation structure on obstacles' edge
In this paper, Reconfigurable Intelligent Surface & Edge (RISE) is proposed to extend RIS' abilities of reflection and refraction over surfaces to diffraction around obstacles' edge for better adaptation to specific coverage scenarios.
Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
However, these methods are only trained at a single granularity (i. e., either token level or span level) and have some weaknesses of the corresponding granularity.
Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning
For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes.
Learning to Classify Open Intent via Soft Labeling and Manifold Mixup
In our method, soft labeling is used to reshape the label distribution of the known intent samples, aiming at reducing model's overconfident on known intents.
A Symmetric Local Search Network for Emotion-Cause Pair Extraction
Each subnetwork is composed of a clause representation learner and a local pair searcher.
Enriching Word Embeddings with Domain Knowledge for Readability Assessment
In this paper, we present a method which learns the word embedding for readability assessment.
