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Phase retrieval (PR) is an important nonlinear inverse problem in scientific imaging. It aims at reconstructing the phase of a signal from its intensity measurements. Recently, there is an increasing interest in deep learning-based PR. Motivated by the challenge of collecting ground-truth (GT) images in many domains, this paper proposes an unsupervised learning approach for PR, which trains a deep end-to-end model without using any GT image. The unsupervised learning from intensity measurements is done via a teacher-student online distillation framework. A teacher model is trained using a self-expressive loss with noise resistance, and a student model is trained with a consistency loss on augmented data to exploit the teacher&#39;s dark knowledge for improvement. An enhanced unfolding neural network is also developed for both the teacher and student models. Extensive experiments show that, the proposed approach not only outperforms existing unsupervised PR methods with higher computational efficiency, but also performs competitively against the supervised ones.

AAAI 2023

Unsupervised Deep Learning for Phase Retrieval via Teacher-Student Distillation

ml: unsupervised & self-supervised learning

cv: low level & physics-based vision

cv: applications

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Phase retrieval (PR) is an important nonlinear inverse problem in scientific imaging. It aims at reconstructing the phase of a signal from its intensity measurements. Recently, there is an increasing interest in deep learning-based PR. Motivated by the challenge of collecting ground-truth (GT) images in many domains, this paper proposes an unsupervised learning approach for PR, which trains a deep end-to-end model without using any GT image. The unsupervised learning from intensity measurements is done via a teacher-student online distillation framework. A teacher model is trained using a self-expressive loss with noise resistance, and a student model is trained with a consistency loss on augmented data to exploit the teacher's dark knowledge for improvement. An enhanced unfolding neural network is also developed for both the teacher and student models. Extensive experiments show that, the proposed approach not only outperforms existing unsupervised PR methods with higher computational efficiency, but also performs competitively against the supervised ones.

poster

Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both image global structures and object details remains a challenging task. In this paper, we present Frido, a Feature Pyramid Diffusion model performing a multi-scale coarse-to-fine denoising process for image synthesis. Our model decomposes an input image into scale-dependent vector quantized features, followed by a coarse-to-fine gating for producing image output. During the above multi-scale representation learning stage, additional input conditions like text, scene graph, or image layout can be further exploited. Thus, Frido can be also applied for conditional or cross-modality image synthesis. We conduct extensive experiments over various unconditioned and conditional image generation tasks, ranging from text-to-image synthesis, layout-to-image, scene-graph-to-image, to label-to-image. More specifically, we achieved state-of-the-art FID scores on five benchmarks, namely layout-to-image on COCO and OpenImages, scene-graph-to-image on COCO and Visual Genome, and label-to-image on COCO.

Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis

Knowledge Tracing (KT) is a crucial task in the field of online education, since it aims to predict students' performance on exercises based on their learning history. One typical solution for knowledge tracing is to combine the classic models in educational psychology, such as Item Response Theory (IRT) and Cognitive Diagnosis (CD), with Deep Neural Networks (DNN) technologies. In this solution, a student and related exercises are mapped into feature vectors based on the student's performance at the current time step, however, it does not consider the impact of historical behavior sequences, and the relationships between historical sequences and students. In this paper, we develop DAKTN, a novel model which assimilates the historical sequences to tackle this challenge for better knowledge tracing. To be specific, we apply a pooling layer to incorporate the student behavior sequence in the embedding layer. After that, we further design a local activation unit, which can adaptively calculate the representation vectors by taking the relevance of historical sequences into consideration with respect to candidate student and exercises. Through experimental results on three real-world datasets, DAKTN significantly outperforms state-of-the-art baseline models. We also present the reasonableness of DAKTN by ablation testing.

Deep Attentive Model for Knowledge Tracing

We study the enumeration of answers to ontology-mediated queries
when the ontology is formulated in a description logic that supports
functional roles and the query is a CQ. In particular, we show that
enumeration is possible with linear preprocessing and constant delay
when a certain extension of the CQ (pertaining to functional roles)
is acyclic and free-connex acyclic. This holds both for complete answers and
for partial answers. We provide matching lower bounds for the
case where the query is self-join free.


Efficient Answer Enumeration in Description Logics with Functional Roles

The challenge of information extraction (IE) lies in the diversity of label schemas and the heterogeneity of structures.
Traditional methods require task-specific model design and rely heavily on expensive supervision, making them difficult to generalize to new schemas.
In this paper, we decouple IE into two basic abilities, structuring and conceptualizing, which are shared by different tasks and schemas.
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing.
In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand.
Empirical evaluation on 4 IE tasks shows that the proposed method achieves state-of-the-art performance under the supervised experiments and shows strong generalization ability in zero/few-shot transfer settings.

Universal Information Extraction as Unified Semantic Matching

Most scanning LiDAR sensors generate a sequence of point clouds in real-time. While conventional 3D object detectors use a set of unordered LiDAR points acquired over a fixed time interval, recent studies have revealed that substantial performance improvement can be achieved by exploiting the spatio-temporal context present in a sequence of LiDAR point sets. In this paper, we propose a novel 3D object detection architecture, which can encode LiDAR point cloud sequences acquired by multiple successive scans. The encoding process of the point cloud sequence is performed on two different time scales. We first design a short-term motion-aware voxel encoding that captures the short-term temporal changes of point clouds driven by the motion of objects in each voxel. We also propose long-term motion-guided bird's eye view (BEV) feature enhancement that adaptively aligns and aggregates the BEV feature maps obtained by the short-term voxel encoding by utilizing the dynamic motion context inferred from the sequence of the feature maps. The experiments conducted on the public nuScenes benchmark demonstrate that the proposed 3D object detector offers significant improvements in performance compared to the baseline methods and that it sets a state-of-the-art performance for certain 3D object detection categories. Code is available at https://github.com/HYjhkoh/MGTANet.git.

MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection

Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We formalize the problem of diagnosing such distributed systems (DDIF) and present distributed versions of two SFL-based algorithms, one for diagnosing problems with a single fault and one for diagnosing multiple faults. We analyze these algortihms theoretically and empirically. Our empirical evaluation shows that the distributed versions output identical diagnoses to the centralized, while preserving private data.

Distributed Spectrum-based Fault Localization

The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from a policy pi. To do this, we need a model of how pi relates to R. In the current literature, the most common models are optimality, Boltzmann rationality, and causal entropy maximisation. One of the primary motivations behind IRL is to infer human preferences from human behaviour. However, the true relationship between human preferences and human behaviour is much more complex than any of the models currently used in IRL. This means that they are misspecified, which raises the worry that they might lead to unsound inferences if applied to real-world data. In this paper, we provide a mathematical analysis of how robust different IRL models are to misspecification, and answer precisely how the demonstrator policy may differ from each of the standard models before that model leads to faulty inferences about the reward function R. We also introduce a framework for reasoning about misspecification in IRL, together with formal tools that can be used to easily derive the misspecification robustness of new IRL models.

Misspecification in Inverse Reinforcement Learning

Current generative transformer-based models have achieved state-of-the-art performance in long document summarization. However, this task witnessed a paradigm shift in developing ever-increasingly computationally-hungry models, focusing on effectiveness while ignoring the economic, environmental, and social costs of producing such results. Consequently, such extensive resources impact climate change and raise barriers to small and medium organizations characterized by low-resource regimes of hardware and data. As a result, this unsustainable trend has lifted many concerns in the community, proposing tools to monitor models' energy costs. Despite their importance, no evaluation measure considering models' eco-sustainability exists yet. In this work, we propose Carburacy, the first carbon-aware accuracy measure that captures both model effectiveness and eco-sustainability. We perform an extensive benchmark for long document summarization, comparing multiple state-of-the-art quadratic and linear transformers on several datasets under eco-sustainable regimes. Finally, thanks to Carburacy, we found optimal combinations of hyperparameters that let models be competitive in effectiveness with significantly lower costs.

Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy

NewsPanda: Media Monitoring for Timely Conservation Action

Traditional machine learning models can be formulated as the {expected risk minimization} (ERM) problem: 
$\min_{\mathbf w\in\mathbb{R}^d}\mathbb E_{\xi}[\ell(\mathbf w; \xi)]$, 
where $\mathbf{w}\in\mathbb{R}^d$ denotes the model parameter, $\xi$ represents training samples, $\ell(\cdot)$ is the loss function. Numerous optimization algorithms, such as stochastic gradient descent (SGD), have been developed to solve the ERM problem. However, a wide range of emerging machine learning models are beyond this class of optimization problems, such as model-agnostic meta-learning. Of particular interest of my research is the \textit{stochastic nested optimization} (\textbf{SNO}) problem, whose objective function has a nested structure. Specifically, I have been focusing on two instances of this kind of problem: \textit{stochastic compositional optimization} (SCO) problems, which cover meta-learning, area-under-the-precision-recall-curve optimization, contrastive self-supervised learning, etc., and \textit{stochastic bilevel optimization} (SBO) problems, which can be applied to meta-learning, hyperparameter optimization, neural network architecture search, etc. 

With the emergence of large-scale distributed data, such as the user data generated on mobile devices or intelligent hardware, it is imperative to develop distributed optimization algorithms for SNO (\textbf{Distributed SNO}). A significant challenge for optimizing distributed SNO problems lies in that the stochastic (hyper-)gradient is a biased estimation of the full gradient. Thus, existing distributed optimization algorithms when applied to them suffer from slow convergence rates. In this talk, I will discuss my recent works about distributed SCO and distributed SBO under both centralized and decentralized settings, including algorithmic details about reducing the bias of stochastic gradient, theoretical convergence rate, and practical machine learning applications, and then highlight challenges for future research.

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Unsupervised Deep Learning for Phase Retrieval via Teacher-Student Distillation

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