Neural Collaborative Subspace Clustering

We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and the other from a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Human-controllable and structured deep generative models

Deep generative models are a class of probabilistic models that attempts to learn the underlying data distribution. These models are usually trained in an unsupervised way and thus, do not require any labels. Generative models such as Variational Autoencoders and Generative Adversarial Networks have made astounding progress over the last years. These models have several benefits: eased sampling and evaluation, efficient learning of low-dimensional representations for downstream tasks, and better understanding through interpretable representations. However, even though the quality of these models has improved immensely, the ability to control their style and structure is limited. Structured and human-controllable representations of generative models are essential for human-machine interaction and other applications, including fairness, creativity, and entertainment. This thesis investigates learning human-controllable and structured representations with deep generative models. In particular, we focus on generative modelling of 2D images. For the first part, we focus on learning clustered representations. We propose semi-parametric hierarchical variational autoencoders to estimate the intensity of facial action units. The semi-parametric model forms a hybrid generative-discriminative model and leverages both parametric Variational Autoencoder and non-parametric Gaussian Process autoencoder. We show superior performance in comparison with existing facial action unit estimation approaches. Based on the results and analysis of the learned representation, we focus on learning Mixture-of-Gaussians representations in an autoencoding framework. We deviate from the conventional autoencoding framework and consider a regularized objective with the Cauchy-Schwarz divergence. The Cauchy-Schwarz divergence allows a closed-form solution for Mixture-of-Gaussian distributions and, thus, efficiently optimizing the autoencoding objective. We show that our model outperforms existing Variational Autoencoders in density estimation, clustering, and semi-supervised facial action detection. We focus on learning disentangled representations for conditional generation and fair facial attribute classification for the second part. Conditional image generation relies on the accessibility to large-scale annotated datasets. Nevertheless, the geometry of visual objects, such as in faces, cannot be learned implicitly and deteriorate image fidelity. We propose incorporating facial landmarks with a statistical shape model and a differentiable piecewise affine transformation to separate the representation for appearance and shape. The goal of incorporating facial landmarks is that generation is controlled and can separate different appearances and geometries. In our last work, we use weak supervision for disentangling groups of variations. Works on learning disentangled representation have been done in an unsupervised fashion. However, recent works have shown that learning disentangled representations is not identifiable without any inductive biases. Since then, there has been a shift towards weakly-supervised disentanglement learning. We investigate using regularization based on the Kullback-Leiber divergence to disentangle groups of variations. The goal is to have consistent and separated subspaces for different groups, e.g., for content-style learning. Our evaluation shows increased disentanglement abilities and competitive performance for image clustering and fair facial attribute classification with weak supervision compared to supervised and semi-supervised approaches.Open Acces

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Controllable music performance synthesis via hierarchical modelling

L’expression musicale requiert le contrôle sur quelles notes sont jouées ainsi que comment elles se jouent. Les synthétiseurs audios conventionnels offrent des contrôles expressifs détaillés, cependant au détriment du réalisme. La synthèse neuronale en boîte noire des audios et les échantillonneurs concaténatifs sont capables de produire un son réaliste, pourtant, nous avons peu de mécanismes de contrôle. Dans ce travail, nous introduisons MIDI-DDSP, un modèle hiérarchique des instruments musicaux qui permet tant la synthèse neuronale réaliste des audios que le contrôle sophistiqué de la part des utilisateurs. À partir des paramètres interprétables de synthèse provenant du traitement différentiable des signaux numériques (Differentiable Digital Signal Processing, DDSP), nous inférons les notes musicales et la propriété de haut niveau de leur performance expressive (telles que le timbre, le vibrato, l’intensité et l’articulation). Ceci donne naissance à une hiérarchie de trois niveaux (notes, performance, synthèse) qui laisse aux individus la possibilité d’intervenir à chaque niveau, ou d’utiliser la distribution préalable entraînée (notes étant donné performance, synthèse étant donné performance) pour une assistance créative. À l’aide des expériences quantitatives et des tests d’écoute, nous démontrons que cette hiérarchie permet de reconstruire des audios de haute fidélité, de prédire avec précision les attributs de performance d’une séquence de notes, mais aussi de manipuler indépendamment les attributs étant donné la performance. Comme il s’agit d’un système complet, la hiérarchie peut aussi générer des audios réalistes à partir d’une nouvelle séquence de notes. En utilisant une hiérarchie interprétable avec de multiples niveaux de granularité, MIDI-DDSP ouvre la porte aux outils auxiliaires qui renforce la capacité des individus à travers une grande variété d’expérience musicale.Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience

Survey of deep representation learning for speech emotion recognition

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER

Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective

Data-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. Here software engineering needs to be re-thought where data becomes a first-class citizen on par with code. One striking observation is that a significant portion of the machine learning process is spent on data preparation. Without good data, even the best machine learning algorithms cannot perform well. As a result, data-centric AI practices are now becoming mainstream. Unfortunately, many datasets in the real world are small, dirty, biased, and even poisoned. In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications. Data collection is important because there is lesser need for feature engineering for recent deep learning approaches, but instead more need for large amounts of data. For data quality, we study data validation, cleaning, and integration techniques. Even if the data cannot be fully cleaned, we can still cope with imperfect data during model training using robust model training techniques. In addition, while bias and fairness have been less studied in traditional data management research, these issues become essential topics in modern machine learning applications. We thus study fairness measures and unfairness mitigation techniques that can be applied before, during, or after model training. We believe that the data management community is well poised to solve these problems