Publications

Multi-objective PSO semi-supervised random forest method for dioxin soft sensor
Wen Xu
Jian Tang
Heng Xia
Wen Yu
JunFei Qiao
2023-12-31
Eng. Appl. Artif. Intell. (publié)
doi.org
Multi-reservoir ESN-based prediction strategy for dynamic multi-objective optimization
Cuili Yang
Danlei Wang
Jian Tang
JunFei Qiao
Wen Yu
2023-12-31
Information Sciences (publié)
doi.org
MVP: Minimal Viable Phrase for Long Text Understanding.
Louis Clouatre
Amal Zouaq
A. Chandar
2023-12-31
International Conference on Language Resources and Evaluation (publié)
dblp.uni-trier.de
Nearest Neighbour Score Estimators for Diffusion Generative Models
Matthew Niedoba
Dylan Green
Saeid Naderiparizi
Vasileios Lioutas
Jonathan Wilder Lavington
Xiaoxuan Liang
Yunpeng Liu
Ke Zhang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most com… (voir plus)monly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a novel nearest neighbour score function estimator which utilizes multiple samples from the training set to dramatically decrease estimator variance. We leverage our low variance estimator in two compelling applications. Training consistency models with our estimator, we report a significant increase in both convergence speed and sample quality. In diffusion models, we show that our estimator can replace a learned network for probability-flow ODE integration, opening promising new avenues of future research. Code will be released upon paper acceptance.
2023-12-31
ICML (publié)
doi.org
proceedings.mlr.press
Neural Implicit Reduced Fluid Simulation
Yuanyuan Tao
Ivan Puhachov
Derek Nowrouzezahrai
Paul Kry
High-fidelity simulation of fluid dynamics is challenging because of the high dimensional state data needed to capture fine details and the … (voir plus)large computational cost associated with advancing the system in time. We present neural implicit reduced fluid simulation (NIRFS), a reduced fluid simulation technique that combines an implicit neural representation of fluid shapes and a neural ordinary differential equation to model the dynamics of fluid in the reduced latent space. The latent trajectories are computed at very little cost in comparison to simulations for training, while preserving fine physical details. We show that this approach can work well, capturing the shapes and dynamics involved in a variety of scenarios with constrained initial conditions, e.g., droplet-droplet collisions, crown splashes, and fluid slosh in a container. In each scenario, we learn the latent implicit representation of fluid shapes with a deep-network signed distance function, as well as the energy function and parameters of a damped Hamiltonian system, which helps guarantee desirable properties of the latent dynamics. To ensure that latent shape representations form smooth and physically meaningful trajectories, we simultaneously learn the latent representation and dynamics. We evaluate novel simulations for conservation of volume and momentum conservation, discuss design decisions, and demonstrate an application of our method to fluid control.
2023-12-31
SIGGRAPH Asia (publié)
doi.org
Neural Semantic Surface Maps
Luca Morreale
Noam Aigerman
Vladimir Kim
Niloy J. Mitra
2023-12-31
Comput. Graph. Forum (publié)
doi.org
arxiv.org
Normalization and effective learning rates in reinforcement learning
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
James Martens
Hado van Hasselt
Razvan Pascanu
Will Dabney
2023-12-31
Advances in Neural Information Processing Systems 37 (publié)
doi.org
openreview.net
Do not trust what you trust: Miscalibration in Semi-supervised Learning
Shambhavi Mishra
Balamurali Murugesan
Ismail Ben Ayed
Marco Pedersoli
Jose Dolz
State-of-the-art semi-supervised learning (SSL) approaches rely on highly confident predictions to serve as pseudo-labels that guide the tra… (voir plus)ining on unlabeled samples. An inherent drawback of this strategy stems from the quality of the uncertainty estimates, as pseudo-labels are filtered only based on their degree of uncertainty, regardless of the correctness of their predictions. Thus, assessing and enhancing the uncertainty of network predictions is of paramount importance in the pseudo-labeling process. In this work, we empirically demonstrate that SSL methods based on pseudo-labels are significantly miscalibrated, and formally demonstrate the minimization of the min-entropy, a lower bound of the Shannon entropy, as a potential cause for miscalibration. To alleviate this issue, we integrate a simple penalty term, which enforces the logit distances of the predictions on unlabeled samples to remain low, preventing the network predictions to become overconfident. Comprehensive experiments on a variety of SSL image classification benchmarks demonstrate that the proposed solution systematically improves the calibration performance of relevant SSL models, while also enhancing their discriminative power, being an appealing addition to tackle SSL tasks.
2023-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
arxiv.org
NOx emissions prediction for MSWI process based on dynamic modular neural network
Haoshan Duan
Xi Meng
Jian Tang
JunFei Qiao
2023-12-31
Expert systems with applications (publié)
doi.org
Offline Multitask Representation Learning for Reinforcement Learning
Raman Arora
Haque Ishfaq
Songtao Feng
Thanh Nguyen-Tang
Doina Precup
Mengdi Wang
Ming Yin
We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from … (voir plus)different tasks that share a common representation and is asked to learn the shared representation. We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning. Furthermore, we examine downstream RL in reward-free, offline and online scenarios, where a new task is introduced to the agent that shares the same representation as the upstream offline tasks. Our theoretical results demonstrate the benefits of using the learned representation from the upstream offline task instead of directly learning the representation of the low-rank model.
2023-12-31
Advances in Neural Information Processing Systems 37 (publié)
doi.org
openreview.net
Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning
Heng Xia
Jian Tang
Wen Yu
JunFei Qiao
Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmen… (voir plus)tal indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning system (FTBLS) is proposed, which includes offline training and online measurement. In the offline training part, weighted k-means is presented to construct a typical sample pool for reduced learning costs of offline and online phases. Moreover, the novel FTBLS, which contains a feature mapping layer, enhance layer, and increment layer, by replacing the fuzzy decision tree with neurons applied to construct the offline model. In the online measurement part, recursive principal component analysis is used to monitor the time-varying characteristic of the MSWI process. To measure DXN emission, offline FTBLS is reused for normal samples; for drift samples, fast incremental learning is used for online updates. A DXN data from the actual MSWI process is employed to prove the usefulness of FTBLS, where the RMSE of training and testing data are 0.0099 and 0.0216, respectively. This result shows that FTBLS can effectively realize DXN online prediction.
2023-12-31
IEEE Transactions on Industrial Informatics (publié)
doi.org
Open-Set Multivariate Time-Series Anomaly Detection
Thomas Lai
Thi Kieu Khanh Ho
Narges Armanfard
2023-12-31
ECAI (publié)
doi.org
arxiv.org