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Found 23 papers, 10 papers with code
Event Graph based Sentence Fusion
Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence.
Lyapunov Neural Network with Region of Attraction Search
Deep learning methods have been widely used in robotic applications, making learning-enabled control design for complex nonlinear systems a promising direction.
Beyond Language Models: Byte Models are Digital World Simulators
Traditional deep learning often overlooks bytes, the basic units of the digital world, where all forms of information and operations are encoded and manipulated in binary format.
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers
Leveraging the knowledge from monolithic models, using techniques such as knowledge distillation, is likely to facilitate the training of modular models and enable them to integrate knowledge from multiple models pretrained on diverse sources.
ChatMusician: Understanding and Generating Music Intrinsically with LLM
It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.
HyperMoE: Paying Attention to Unselected Experts in Mixture of Experts via Dynamic Transfer
The Mixture of Experts (MoE) for language models has been proven effective in augmenting the capacity of models by dynamically routing each input token to a specific subset of experts for processing.
A Novel Garment Transfer Method Supervised by Distilled Knowledge of Virtual Try-on Model
This paper proposes a novel garment transfer method supervised with knowledge distillation from virtual try-on.
Evolving Large Language Model Assistant with Long-Term Conditional Memory
It focuses on preserving the knowledge and experience from the history dialogue between the user and AI assistant, which can be applied to future dialogue for generating a better response.
GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation
To address these issues, we propose the GSDC Transformer, an efficient and effective component for cue fusion in monocular multi-frame depth estimation.
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs).
Ranked #6 on
Code Generation
on HumanEval
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs).
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT
Therefore, we propose a method called RefGPT to generate enormous truthful and customized dialogues without worrying about factual errors caused by the model hallucination.
Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
Computational models of syntax are predominantly text-based.
PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm
To address these limitations, we propose a novel virtual try-on method via progressive inference paradigm (PGVTON) that leverages a top-down inference pipeline and a general garment try-on strategy.
A Cross-Scale Hierarchical Transformer with Correspondence-Augmented Attention for inferring Bird's-Eye-View Semantic Segmentation
To save the computation increase caused by this hierarchical framework, we exploit the cross-scale Transformer to learn feature relationships in a reversed-aligning way, and leverage the residual connection of BEV features to facilitate information transmission between scales.
Few-shot Query-Focused Summarization with Prefix-Merging
Drawn inspiration from prefix-tuning, we are allowed to integrate the task knowledge from text summarization and question answering into a properly designed prefix and apply the merged prefix to query-focused summarization.
Physical Logic Enhanced Network for Small-Sample Bi-Layer Metallic Tubes Bending Springback Prediction
In the second stage, under the physical logic, the PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small sample BMT dataset and composite loss function.
Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL
To address this issue, a digital-twin-enhanced (DT-enhanced) metal tube bending forming real-time prediction method based on multi-source-input multi-task learning (MTL) is proposed.
Bearing-Based Formation Control with Optimal Motion Trajectory
We form and solve a nonlinear optimization problem with the sum of path lengths of the agent trajectories as the objective and subject to the original equilibria and global convergence conditions for formation control.
Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT
We also introduce a hierarchical structure, which incorporates the multi-level of granularities of the textual information into the model.
Query-aware Tip Generation for Vertical Search
Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios.
MSnet: A BERT-based Network for Gendered Pronoun Resolution
In stage 1 of the gendered pronoun resolution task, a variant of this model, trained in the fine-tuning approach, reduced the multi-class logarithmic loss to 0. 3033 in the 5-fold cross-validation of training set and 0. 2795 in testing set.
