Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks59 from public data
- Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study351
The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
- On penalty-based bilevel gradient descent method137
This work shows that under certain conditions, the penalty reformulation recovers the (local) solutions of the original bilevel problem and proposes the penalty-based bilevel gradient descent (PBGD) algorithm and establishes its finite-time convergence for the constrained bilevel problem with lower-level constraints yet without lower-level strong convexity.
- 99
- Recurrent Exposure Generation for Low-Light Face Detection95
The proposed method, named REGDet, is the first ‘detection-with-enhancement’ framework for low-light face detection and not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection.
- LOGER: A Learned Optimizer Towards Generating Efficient and Robust Query Execution Plans62
LOGER, a learned optimizer towards generating efficient and robust plans, aiming at producing both efficient join orders and operators, is proposed, and can achieve a performance better than existing learned query optimizers.
- Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction57
This work devised a selective classification approach and integrated it with the Hierarchical Interaction Network, the state-of-the-art clinical trial prediction model, and demonstrated that incorporating uncertainty markedly enhances the model’s performance.
- OTOV2: Automatic, Generic, User-Friendly54
The second generation of Only-Train-Once (OTOv2), which first automatically trains and compresses a general DNN only once from scratch to produce a more compact model with competitive performance without fine-tuning, is proposed.
- 39
- Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression36
The framework GETA is presented, which automatically and efficiently performs joint structured pruning and quantization- aware training on any DNN, and introduces three key innovations: a quantization-aware dependency graph (QADG) that constructs a pruning search space for generic quantization-aware DNN, and a partially projected stochastic gradient method that guarantees layerwise bit constraints are satisfied.
- Uncertainty Quantification on Clinical Trial Outcome Prediction34
It is demonstrated that incorporating selective classification into clinical trial predictions markedly enhances the model's performance, as evidenced by significant upticks in pivotal metrics such as PR-AUC, F1, ROC-AUC, and overall accuracy, and is potentially setting a new benchmark in the field.
- Orthant Based Proximal Stochastic Gradient Method for $$\ell _1$$-Regularized Optimization30
On a large number of convex problems, OBProx-SG outperforms the existing methods comprehensively in the aspect of sparsity exploration and objective values, and the experiments on non-convex deep neural networks further demonstrate its superiority by achieving the solutions of much higher sparsity without sacrificing generalization accuracy.
- A Reduced-Space Algorithm for Minimizing $\ell_1$-Regularized Convex Functions30
A convergence guarantee is proved for the new method for minimizing the sum of a differentiable convex function and an $\ell_1$-norm regularizer and its efficiency is demonstrated on a large set of model prediction problems.
- Evolution of co-operation among mobile agents with different influence19
This work mainly focuses on the weighted influence on the mobility of agents in the prisoner’s dilemma game, and proves that co-operation level is promoted when the heterogeneous influence factor is considered.
- DRBench: A Realistic Benchmark for Enterprise Deep Research18
The DRBench benchmark is introduced, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings that require identifying supporting facts from both the public web and private company knowledge base.
- Extensible Cross-Modal Hashing18
This work proposes a novel extensible cross-modal hashing (ECMH) model that has good forward compatibility, so there is no need to update old hash codes, and is extended to support new data categories using only new data by a well-designed “weak constraint incremental learning” algorithm.
- 17
- 17
- 17
- 16
- Single-image raindrop removal using concurrent channel-spatial attention and long-short skip connections16
A concurrent channel and spatial attention mechanism implemented by Squeeze & Excitation into the network is introduced to address the challenges of raindrop removal and the experimental results show that the proposed approach outperformed the existing ones.
- FaRSA for ℓ 1 -regularized convex optimization: local convergence and numerical experience15
An enhanced subproblem termination condition is introduced that allows us to prove that the iterates converge locally at a superlinear rate and the details of the publicly available C implementation are presented along with extensive numerical comparisons to other state-of-the-art solvers.
- 13
- 13
- 12
- A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition11
A stationary metric is introduced for the considered bilevel problems, which generalizes the existing metric, and it is established that GALET achieves an (cid:15) -stationary metric for the considering problem within ˜ O ( ( cid: 15) − 1 ) iterations, which matches the iteration complexity of GD for smooth nonconvex problems.
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.