Unsupervised structure induction and multimodal grounding

Structured representations build upon symbolic abstraction (e.g., words in natural language and visual concepts in natural images), offer a principled way of encoding our perceptions about the physical world, and enable the human-like generalization of machine learning systems. The predominant paradigm for learning structured representations of the observed data has been supervised learning, but it is limited in several respects. First, supervised learning is challenging given the scarcity of labeled data. Second, conventional approaches to structured prediction have been relying on a single modality (e.g., either images or text), ignoring the learning cues that may have been specified in and can be readily obtained from other modalities of data. In this thesis, we investigate unsupervised approaches to structure induction in a multimodal setting. Unsupervised learning is inherently difficult in general, let alone inducing complex and discrete structures from data without direct supervision. By considering the multimodal setting, we leverage the alignments between different data modalities (e.g., text, audio, and images) to facilitate the learning of structure-induction models, e.g., knowing that the individual words in ``a white pigeon'' always appear with the same visual object, a language parser is likely to treat them as a whole (i.e., phrase). The multimodal learning setting is practically viable because multimodal alignments are generally abundant. For example, they can be found in online posts such as news and tweets that usually contain images and associated text, and in (YouTube) videos, where audio, scripts, and scenes are synchronized and grounded in each other. We develop structure-induction models, which are capable of exploiting bimodal image-text alignments, for two modalities: (1) for natural language, we consider unsupervised syntactic parsing with phrase-structure grammars and regularize the parser by using visual image groundings; and (2) for visual images, we induce scene graph representations by mapping arguments and predicates in the text to their visual counterparts (i.e., visual objects and relations among them) in an unsupervised manner. While useful, crossmodal alignments are not always abundantly available on the web, e.g., the alignments between non-speech audio and text. We tackle the challenge by sharing the visual modality between image-text alignment and image-audio alignment; images function as a pivot and connect audio and text. The contributions of this thesis span from model development to data collection. We demonstrated the feasibility of applying multimodal learning techniques to unsupervised structure induction and multimodal alignment collection. Our work opens up new avenues for multimodal and unsupervised structured representation learning

Meta-Learning in Neural Networks: A Survey

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research

LOOKING INTO ACTORS, OBJECTS AND THEIR INTERACTIONS FOR VIDEO UNDERSTANDING

Automatic video understanding is critical for enabling new applications in video surveillance, augmented reality, and beyond. Powered by deep networks that learn holistic representations of video clips, and large-scale annotated datasets, modern systems are capable of accurately recognizing hundreds of human activity classes. However, their performance significantly degrades as the number of actors in the scene or the complexity of the activities increases. Therefore, most of the research thus far has focused on videos that are short and/or contain a few activities performed only by adults. Furthermore, most current systems require expensive, spatio-temporal annotations for training. These limitations prevent the deployment of such systems in real-life applications, such as detecting activities of people and vehicles in an extended surveillance videos. To address these limitations, this thesis focuses on developing data-driven, compositional, region-based video understanding models motivated by the observation that actors, objects and their spatio-temporal interactions are the building blocks of activities and the main content of video descriptions provided by humans. This thesis makes three main contributions. First, we propose a novel Graph Neural Network for representation learning on heterogeneous graphs that encode spatio-temporal interactions between actor and object regions in videos. This model can learn context-aware representations for detected actors and objects, which we leverage for detecting complex activities. Second, we propose an attention-based deep conditional generative model of sentences, whose latent variables correspond to alignments between words in textual descriptions of videos and object regions. Building upon the framework of Conditional Variational Autoencoders, we train this model using only textual descriptions without bounding box annotations, and leverage its latent variables for localizing the actors and objects that are mentioned in generated or ground-truth descriptions of videos. Finally, we propose an actor-centric framework for real-time activity detection in videos that are extended both in space and time. Our framework leverages object detections and tracking to generate actor-centric tubelets, capturing all relevant spatio-temporal context for a single actor, and detects activities per tubelet based on contextual region embeddings. The models described have demonstrably improved the ability to temporally detect activities, as well as ground words in visual inputs

Graph-based broad-coverage semantic parsing

Many broad-coverage meaning representations can be characterized as directed graphs, where nodes represent semantic concepts and directed edges represent semantic relations among the concepts. The task of semantic parsing is to generate such a meaning representation from a sentence. It is quite natural to adopt a graph-based approach for parsing, where nodes are identified conditioning on the individual words, and edges are labeled conditioning on the pairs of nodes. However, there are two issues with applying this simple and interpretable graph-based approach for semantic parsing: first, the anchoring of nodes to words can be implicit and non-injective in several formalisms (Oepen et al., 2019, 2020). This means we do not know which nodes should be generated from which individual word and how many of them. Consequently, it makes a probabilistic formulation of the training objective problematical; second, graph-based parsers typically predict edge labels independent from each other. Such an independence assumption, while being sensible from an algorithmic point of view, could limit the expressiveness of statistical modeling. Consequently, it might fail to capture the true distribution of semantic graphs. In this thesis, instead of a pipeline approach to obtain the anchoring, we propose to model the implicit anchoring as a latent variable in a probabilistic model. We induce such a latent variable jointly with the graph-based parser in an end-to-end differentiable training. In particular, we test our method on Abstract Meaning Representation (AMR) parsing (Banarescu et al., 2013). AMR represents sentence meaning with a directed acyclic graph, where the anchoring of nodes to words is implicit and could be many-to-one. Initially, we propose a rule-based system that circumvents the many-to-one anchoring by combing nodes in some pre-specified subgraphs in AMR and treats the alignment as a latent variable. Next, we remove the need for such a rule-based system by treating both graph segmentation and alignment as latent variables. Still, our graph-based parsers are parameterized by neural modules that require gradient-based optimization. Consequently, training graph-based parsers with our discrete latent variables can be challenging. By combing deep variational inference and differentiable sampling, our models can be trained end-to-end. To overcome the limitation of graph-based parsing and capture interdependency in the output, we further adopt iterative refinement. Starting with an output whose parts are independently predicted, we iteratively refine it conditioning on the previous prediction. We test this method on semantic role labeling (Gildea and Jurafsky, 2000). Semantic role labeling is the task of predicting the predicate-argument structure. In particular, semantic roles between the predicate and its arguments need to be labeled, and those semantic roles are interdependent. Overall, our refinement strategy results in an effective model, outperforming strong factorized baseline models

Deep Causal Learning: Representation, Discovery and Inference

Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work

Composing Deep Learning and Bayesian Nonparametric Methods

Recent progress in Bayesian methods largely focus on non-conjugate models featured with extensive use of black-box functions: continuous functions implemented with neural networks. Using deep neural networks, Bayesian models can reasonably fit big data while at the same time capturing model uncertainty. This thesis targets at a more challenging problem: how do we model general random objects, including discrete ones, using random functions? Our conclusion is: many (discrete) random objects are in nature a composition of Poisson processes and random functions}. Thus, all discreteness is handled through the Poisson process while random functions captures the rest complexities of the object. Thus the title: composing deep learning and Bayesian nonparametric methods. This conclusion is not a conjecture. In spacial cases such as latent feature models , we can prove this claim by working on infinite dimensional spaces, and that is how Bayesian nonparametric kicks in. Moreover, we will assume some regularity assumptions on random objects such as exchangeability. Then the representations will show up magically using representation theorems. We will see this two times throughout this thesis. One may ask: when a random object is too simple, such as a non-negative random vector in the case of latent feature models, how can we exploit exchangeability? The answer is to aggregate infinite random objects and map them altogether onto an infinite dimensional space. And then assume exchangeability on the infinite dimensional space. We demonstrate two examples of latent feature models by (1) concatenating them as an infinite sequence (Section 2,3) and (2) stacking them as a 2d array (Section 4). Besides, we will see that Bayesian nonparametric methods are useful to model discrete patterns in time series data. We will showcase two examples: (1) using variance Gamma processes to model change points (Section 5), and (2) using Chinese restaurant processes to model speech with switching speakers (Section 6). We also aware that the inference problem can be non-trivial in popular Bayesian nonparametric models. In Section 7, we find a novel solution of online inference for the popular HDP-HMM model

Generative factorization for object-centric representation learning

Empowering machines to understand compositionality is considered by many (Lake et al., 2017; Lake and Baroni, 2018; Schölkopf et al., 2021) a promising path towards improved representational interpretability and out-of-distribution generalization. Yet, discovering the compositional structures of raw sensory data requires solving a factorization problem, i.e. decomposing the unstructured observations into modular components. Handling the factorization problem presents numerous technical challenges, especially in unsupervised settings which we explore to avoid the heavy burden of human annotation. In this thesis, we approach the factorization problem from a generative perspective. Specifically, we develop unsupervised machine learning models to recover the compositional data-generation mechanisms around objects from visual scene observations. First, we present MulMON as the first feasible unsupervised solution to the multi-view object-centric representation learning problem. MulMON resolves the spatial ambiguities arising from single-image observations of static scenes, e.g. optical illusions and occlusion, with a multi-view inference design. We demonstrate that not only can MulMON perform better scene object factorization with less uncertainty than single-view methods, but it can also predict a scene's appearances and object segmentations for novel viewpoints. Next, we present a technique, namely for latent duplicate suppression (abbr. LDS), and demonstrate its effectiveness in fixing a common scene object factorization issue that exists in various unsupervised object-centric learning models---i.e. inferring duplicate representations for the same objects. Finally, we present DyMON as the first unsupervised learner that can recover object-centric compositional generative mechanism from moving-view-dynamic-scene observational data. We demonstrate that not only can DyMON factorize dynamic scenes in terms of objects, but it can also factorize the entangled effects of observer motions and object dynamics that function independently. Furthermore, we demonstrate that DyMON can predict a scene's appearances and segmentations at arbitrary times (querying across time) and from arbitrary viewpoints (querying across space)---i.e. answer counterfactual questions. The scene modeling explored in this thesis is a proof of concept, which we hope will inspire: 1) a broader range of downstream applications (e.g. "world modelling'' and environment interactions) and 2) generative factorization research that targets more complex compositional structures (e.g. complex textures, multi-granularity compositions)

Invariance in deep representations

In this thesis, Invariance in Deep Representations, we propose novel solutions to the problem of learning invariant representations. We adopt two distinct notions of invariance. One is rooted in symmetry groups and the other in causality. Last, despite being developed independently from each other, we aim to take a first step towards unifying the two notions of invariance. The thesis consists of four main sections where: (i) We propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. We develop a novel approach for set classification. (ii) We demonstrate that causal concepts can be used to explain the success of data augmentation by describing how they can weaken the spurious correlation between the observed domains and the task labels. We demonstrate that data augmentation can serve as a tool for simulating interventional data. (iii) We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders with a single latent confounder that lives in the same space as the treatment variable without changing the observational and interventional distributions entailed by the causal model. After the reduction, we parameterize the reduced causal model using a flexible class of transformations, so-called normalizing flows. (iv) We propose the Domain Invariant Variational Autoencoder, a generative model that tackles the problem of domain shifts by learning three independent latent subspaces, one for the domain, one for the class, and one for any residual variations

Machine learning in solar physics

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP