Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little is devoted to modeling the time evolving target domain, such as the online reviews for movies. To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain. One major challenge associated with continuous transfer learning is the potential occurrence of negative transfer as the target domain evolves over time. To address this challenge, we propose a novel label-informed C-divergence between the source and target domains in order to measure the shift of data distributions as well as to identify potential negative transfer. We then derive the error bound for the target domain using the empirical estimate of our proposed C-divergence. Furthermore, we propose a generic adversarial Variational Auto-encoder framework named TransLATE by minimizing the classification error and C-divergence of the target domain between consecutive time stamps in a latent feature space. In addition, we define a transfer signature for characterizing the negative transfer based on C-divergence, which indicates that larger C-divergence implies a higher probability of negative transfer in real scenarios. Extensive experiments on synthetic and real data sets demonstrate the effectiveness of our TransLATE framework.
Utah State University（犹他州立大学）；Université de Montréal（蒙特利尔大学）
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI can be considered as belonging to a middle ground between the more commonly addressed semi-supervised manifold alignment problem wit…
Imagia（Imagia公司）；Dalhousie University（达尔豪斯大学）；Mila, Université de Montréal（蒙特利尔大学米拉研究所）；Polish Academy of Sciences（波兰科学院）
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. However, these methods suffer from pseudo-label bias in the form of error accumulation. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly.…
The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with identical label space is a challenging task. Partial domain adaptation alleviates this problem of procuring a labeled dataset with identical label space assumptions and addresses a more practical scenario where the source label set subsumes the target label set. This, however, presents a few additional obstacles during adaptation. Samples with categor…
Physics-Informed Deep Learning and Partial Transfer Learning for Bearing Fault Diagnosis in the Presence of Highly Missing Data
Faculty of Electrical and Computer Engineering, Tarbiat Modares University（塔里克·莫达雷斯大学电气与计算机工程学院）；Faculty of Technology and Engineering, University of Mazandaran（马赞德兰大学技术与工程学院）
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；PhiGent Robotics（鉴智机器人）
Data mixing strategies (e.g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs). They mix two images as inputs for training and assign them with a mixed label with the same ratio. While they are shown effective for vision transformers (ViTs), we identify a token fluctuation phenomenon that has suppressed the potential of data mixing strategies. We empirically observe that the contributions of input tokens fluctuate as forward propagating, which might induce a different mixing ratio in the output tokens. The training target computed by the…
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）
Domain adaptation (DA) is used for adaptively obtaining labels of an unprocessed data set with a given related, but different labelled data set. Subspace alignment (SA), a representative DA algorithm, attempts to find a linear transformation to align the subspaces of the two different data sets. The classifier trained on the aligned labelled data set can be transferred to the unlabelled data set to predict the target labels. In this paper, two quantum versions of the SA are proposed to implement the DA procedure on quantum devices. One method, the quantum subspace alignment algorithm (QSA), ac…
Huazhong University of Science and Technology（华中科技大学）
A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature…
By integrating domain knowledge with labeled samples, informed machine learning has been emerging to improve the learning performance for a wide range of applications. Nonetheless, rigorous understanding of the role of injected domain knowledge has been under-explored. In this paper, we consider an informed deep neural network (DNN) with over-parameterization and domain knowledge integrated into its training objective function, and study how and why domain knowledge benefits the performance. Concretely, we quantitatively demonstrate the two benefits of domain knowledge in informed learning - r…
We present label gradient alignment, a novel algorithm for semi-supervised learning which imputes labels for the unlabeled data and trains on the imputed labels. We define a semantically meaningful distance metric on the input space by mapping a point (x, y) to the gradient of the model at (x, y). We then formulate an optimization problem whose objective is to minimize the distance between the labeled and the unlabeled data in this space, and we solve it by gradient descent on the imputed labels. We evaluate label gradient alignment using the standardized architecture introduced by Oliver et a…
Density-Informed VAE (DiVAE): Reliable Log-Prior Probability via Density Alignment Regularization
Department of Mathematics, Informatics and Geosciences, University of Trieste（特里埃斯特大学数学、信息学与地球科学系）；Laboratory of Data Engineering, Area Science Park, Trieste, Italy（数据工程实验室，特里埃斯特科学园）
Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所智能感知与计算研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only consider feature information while ignoring available label information. In this paper, we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix. We conduct extensive experiments on seve…
We propose Jointly trained Duration Informed Transformer (JDI-T), a feed-forward Transformer with a duration predictor jointly trained without explicit alignments in order to generate an acoustic feature sequence from an input text. In this work, inspired by the recent success of the duration informed networks such as FastSpeech and DurIAN, we further simplify its sequential, two-stage training pipeline to a single-stage training. Specifically, we extract the phoneme duration from the autoregressive Transformer on the fly during the joint training instead of pretraining the autoregressive mode…
Kernel alignment measures the degree of similarity between two kernels. In this paper, inspired from kernel alignment, we propose a new Linear Discriminant Analysis (LDA) formulation, kernel alignment LDA (kaLDA). We first define two kernels, data kernel and class indicator kernel. The problem is to find a subspace to maximize the alignment between subspace-transformed data kernel and class indicator kernel. Surprisingly, the kernel alignment induced kaLDA objective function is very similar to classical LDA and can be expressed using between-class and total scatter matrices. This can be extend…
Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information g…
Label-Informed Outlier Detection Based on Granule Density
College of Computer Science, Sichuan University（四川大学计算机学院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；Sichuan National Innovation New Vision UHD Video Technology Co., Ltd.（四川国家创新新视野UHD视频技术有限公司）
Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
Institution for Foundation of Data Science, Yale University（数据科学基础研究所，耶鲁大学）；Penn Institute for Computational Science, University of Pennsylvania（宾夕法尼亚大学计算科学研究所）；Department of Mathematics, City University of Hong Kong（香港城市大学数学系）；Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania（宾夕法尼亚大学机械工程与应用力学系）
We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier with auxiliary capacity in a manner designed to significantly improve upon an already well-performing model, while minimally impacting its computational footprint. Using the predictions of the network itself as a descriptor for assessing visual similarity, we define a partitioning of the label space into groups of visually similar entities. We then augment the network with auxilliary hidden layer pathways with connec…
Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）深圳市视觉目标检测与识别重点实验室）；School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Mila, Université de Montréal（蒙特利尔大学Mila实验室）；Max Planck Institute for Intelligent Systems - Tübingen（智能系统马克斯·普朗克研究所（图宾根））；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；University of Tübingen（图宾根大学）；University of Cambridge（剑桥大学）；Microsoft Research（微软研究院）
Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment
Department of Mathematics and Statistics, Université de Montréal, Montreal, Canada（蒙特利尔大学数学与统计学系）；Mila - Quebec AI Institute, Montreal, Canada（魁北克人工智能研究所）；Department of Mathematics and Statistics, Utah State University, Logan, Utah, USA（犹他州立大学数学与统计学系）；Department of Statistics, Brigham Young University, Provo, Utah, USA（Brigham Young 大学统计学系）
Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学符号计算与知识工程教育部重点实验室）；Department of Computer Science, Portland State University（波特兰州立大学计算机科学系）
RANA: Robust Active Learning for Noisy Network Alignment
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学网络安全学院）
As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission, EC, numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database. Some interactions are attributed to natural selection and involve the enzyme's natural substrates. The majority of the interactions however involve non-natural substrates, thus reflec…
Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室）；Department of Computer Science, Portland State University（波特兰州立大学计算机科学系）
This paper proposes a novel label-synchronous speech-to-text alignment technique for automatic speech recognition (ASR). The speech-to-text alignment is a problem of splitting long audio recordings with un-aligned transcripts into utterance-wise pairs of speech and text. Unlike conventional methods based on frame-synchronous prediction, the proposed method re-defines the speech-to-text alignment as a label-synchronous text mapping problem. This enables an accurate alignment benefiting from the strong inference ability of the state-of-the-art attention-based encoder-decoder models, which cannot…
NeuroSpin, CEA Saclay, Université Paris-Saclay（法国原子能和替代能源委员会萨克雷中心 NeuroSpin，巴黎萨克雷大学）；LTCI, Télécom Paris, IPParis（巴黎电信学院 LTCI，巴黎理工学院）
Contrastive Learning has shown impressive results on natural and medical images, without requiring annotated data. However, a particularity of medical images is the availability of meta-data (such as age or sex) that can be exploited for learning representations. Here, we show that the recently proposed contrastive y-Aware InfoNCE loss, that integrates multi-dimensional meta-data, asymptotically optimizes two properties: conditional alignment and global uniformity. Similarly to [Wang, 2020], conditional alignment means that similar samples should have similar features, but conditionally on the…
Most existing metric learning methods focus on learning a similarity or distance measure relying on similar and dissimilar relations between sample pairs. However, pairs of samples cannot be simply identified as similar or dissimilar in many real-world applications, e.g., multi-label learning, label distribution learning. To this end, relation alignment metric learning (RAML) framework is proposed to handle the metric learning problem in those scenarios. But RAML learn a linear metric, which can't model complex datasets. Combining with deep learning and RAML framework, we propose a hierarchica…
Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics
School of Electronic and Electrical Engineering, University of Leeds（利兹大学电子与电气工程学院）；Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, School of Mechanical Engineering, Tianjin University（教育部机制理论与装备设计重点实验室，天津大学机械工程学院）；College of Intelligent Systems Science and Engineering, Hubei Minzu University（湖北民族大学智能系统科学与工程学院）
