Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks86 from public data
- Post-Training Quantization on Diffusion Models376
This work accelerates generation of diffusion model (DM) generation from the perspective of compressing the noise estimation network and devise a DM-specific PTQ method, which especially targets the unique multi-time-step structure of DMs.
- LLM Inference Unveiled: Survey and Roofline Model Insights232
This survey systematically collates the latest advancements in efficient LLM inference, covering crucial areas such as model compression, algorithm improvements, and both hardware and system-level enhancements, and introduces a framework based on roofline model for systematic analysis of LLM inference techniques.
- RPTQ: Reorder-based Post-training Quantization for Large Language Models148
This paper identifies that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers, and introduces a quantization method called RPTQ, which utilizes a reorder-based approach.
- Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification132
This paper proposes VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models.
- Parameter-Efficient Fine-Tuning with Discrete Fourier Transform91
This work introduces FourierFT, which treats $\Delta W$ as a matrix in the spatial domain and learns only a small fraction of its spectral coefficients, and implements the inverse discrete Fourier transform to recover $\Delta W$.
- HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation89
This work proposes an agile co-design approach HASCO that provides an efficient HW/SW solution to dense tensor computation and develops a multi-objective Bayesian optimization algorithm to explore hardware optimization.
- ASFGNN: Automated separated-federated graph neural network89
An Automated Separated-Federated Graph Neural Network (ASFGNN) learning paradigm is proposed, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that was learnt by clients federally.
- DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations81
DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets.
- Practical Privacy Preserving POI Recommendation78
This article proposes a novel Privacy preserving POI Recommendation (PriRec) framework that keeps users’ private raw data and models in users' own hands, and protects user privacy to a large extent, and applies PriRec in real-world datasets, and comprehensive experiments demonstrate that it achieves comparable or even better recommendation accuracy.
- Uncertainty in Natural Language Processing: Sources, Quantification, and Applications67
This survey provides a comprehensive review of uncertainty-relevant works in the NLP field and categorizes the sources of uncertainty in natural language into three types, including input, system, and output.
- Calibrating Multimodal Learning52
This paper finds that the confidence estimated by current models could even increase when some modalities are corrupted, and proposes a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods.
- P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification45
A novel stochastic gradient descent scheme, named patient privacy preserving SGD (P3SGD), which performs the model update of the SGD in the patient level via a large-step update built upon each patient's data to protect privacy and regularize the CNN model.
- Secure Social Recommendation Based on Secret Sharing44
A SEcure SOcial RECommendation (SeSoRec) framework which can collaboratively mine knowledge from social platform to improve the recommendation performance of the rating platform, and securely keep the raw data of both platforms is proposed and its correctness and security theoretically is proved.
- Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning43
This paper presents a complete process, from data collection and construction to model training and overfitting elimination, for fine-tuning LLMs in vertical domain deployments, and reveals that incorporating reasoning processes during the fine-tuning of LLMs can effectively alleviate overfitting.
- 39
- 39
- Privacy-Preserving Graph Neural Network for Node Classification.39
This paper proposes a Privacy-Preserving GNN (PPGNN) learning paradigm for node classification task, which can be generalized to existing GNN models and demonstrates that PPGNN significantly outperforms theGNN models trained on the isolated data and has comparable performance with the traditional GNNtrained on the mixed plaintext data.
- 39
- A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection33
This survey provides a comprehensive review of recent leading approaches in the TwGL field from three dimensions, namely, reliability, explainability, and privacy protection, and gives a general categorization for existing work and review typical work for each category.
- S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search33
This paper introduces a general framework, S2DNAS, which can transform various static CNN models to support dynamic inference via neural architecture search and proposes a reinforcement learning based approach to automatically search for the optimal CNN architecture in the generated space.
- 31
- NAS4RRAM: neural network architecture search for inference on RRAM-based accelerators31
This work takes the idea of network architecture search (NAS) to design networks with high prediction accuracy that meet the requirements of the RRAM-based accelerators, and proposes a framework called NAS4RRAM to search for the optimal network on the given R RAM-based accelerator.
- 31
- Reducing Overfitting in Deep Convolutional Neural Networks Using Redundancy Regularizer27
A novel regularizer is proposed to reduce kernel redundancy in a deep CNN model and prevent overfitting and results demonstrate that kernel redundancy is significantly removed and overfitting is substantially reduced with even better performance achieved.
- NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction26
This work systematically explore the use of dual-channel speech data in the context of modern large language models, and introduces a novel generative modeling paradigm, Next-Token-Pair Prediction (NTPP), to enable speaker-independent dual-channel spoken dialogue learning using decoder-only architectures for the first time.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.