Italy

In this talk, I will make connections between Graph Neural Networks (GNNs) and non-Euclidean diffusion equations. I will show that drawing on methods from the domain of differential geometry, it is possible to provide a principled view on such GNN architectural choices as positional encoding and graph rewiring as well as explain and remedy the phenomena of oversquashing and bottlenecks.

IMPROVE 2022

Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

keynote

IMPROVE is a comprehensive conference of academic and technical nature, focused on image processing and computer vision practical applications. It brings together researchers, engineers and practitioners working either in fundamental areas of image processing, developing new methods and techniques, including innovative machine learning approaches, as well as multimedia communications technology and applications of image processing and artificial vision in diverse areas.

In many computer vision applications, the following problem arises: a collection of observations is interpreted as multiple noisy instances of different classes of objects. The observations can be 2D keypoints in an RG B image, 3D points in lidar scans, 2D-2D point correspondences in stereo matching, trajectories in tracking, etc. The classeses of object can be 2D or 3D geometric entitities like lines and planes, or transformations and relations between images. We will review recent techniques, mainly based on the RANSAC approach, for estimation of single and multiple instances of such object and demostrate their performance on a range of practical problems.

Robust Fitting in Computer Vision

Advancements in reliable navigation and mapping rest to a large extent on robust, efficient and scalable understanding of the surrounding environment. The success in recent years have been propelled by the use machine learning techniques for capturing geometry and semantics of environment from video and range sensors. I will discuss approaches object detection, pose recovery, 3D reconstruction and detailed semantic parsing using deep convolutional neural networks (CNNs) as well as challenges of deploying these systems in real-world settings. To overcome the need for large amount of labeled data for training object instance detectors we use active self-supervision provided by a robot traversing an environment and generate training examples for learning novel object embeddings from unlabelled data. The object detectors trained in this manner achieve higher mAP compared to off-the-shelf detectors trained on this limited data. While perception and decision making are often considered separately, I will outline few strategies for jointly optimizing perception and decision making algorithms in the context of elementary navigation tasks. The presented explorations open interesting avenues for control of embodied physical agents and general strategies for design and development of general purpose autonomous systems.

Challenges and Opportunities in Autonomous Navigation

In 1948, Shannon published a famous paper, which laid the foundations of information theory and led to a revolution in communication technologies. Critical to Shannon’s ideas was the notion that a signal can be represented in terms of “bits,” and that the information content of the signal can be measured by the minimum expected number of bits. However, while such a notion of information is well suited for tasks such as signal compression and reconstruction, it is not directly applicable to audio-visual scene interpretation tasks, because bits do not depend on the “semantic content” of the signal, such as words in a document, or objects in an image. In this talk, I will present a new measure of semantic information content called “semantic entropy”, which is defined as the minimum expected number of semantic queries about the data whose answers are sufficient for solving a given task (e.g., classification). I will also present an information-theoretic framework called ``information pursuit'' for deciding which queries to ask and in which order, which requires a probabilistic generative model relating data and questions to the task. Experiments on handwritten digit classification show, for example, that the translated MNIST dataset is harder to classify than the MNIST dataset. Joint work with Aditya Chattopadhyay, Benjamin Haeffele and Donald Geman.

Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks

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