There is a recent focus on designing architectures that have an Integer Linear Programming (ILP) layer within a neural model (referred to as Neural ILP in this paper). Neural ILP architectures are suitable for pure reasoning tasks that require data-driven constraint learning or for tasks requiring both perception (neural) and reasoning (ILP). A recent SOTA approach for end-to-end training of Neural ILP explicitly defines gradients through the ILP black box (Paulus et al. 2021) - this trains extremely slowly, owing to a call to the underlying ILP solver for every training data point in a minibatch. In response, we present an alternative training strategy that is solver-free, i.e., does not call the ILP solver at all at training time. Neural ILP has a set of trainable hyperplanes (for cost and constraints in ILP), together representing a polyhedron. Our key idea is that the training loss should impose that the final polyhedron separates the positives (all constraints satisfied) from the negatives (at least one violated constraint or a suboptimal cost value), via a soft-margin formulation. While positive example(s) are provided as part of the training data, we devise novel techniques for generating negative samples. Our solution is flexible enough to handle equality as well as inequality constraints. Experiments on several problems, both perceptual as well as symbolic, which require learning the constraints of an ILP, show that our approach has superior performance and scales much better compared to purely neural baselines and other state-of-the-art models that require solver-based training. In particular, we are able to obtain excellent performance in 9 x 9 symbolic and visual sudoku, to which the other Neural ILP solver is not able to scale.

Ready or not, health care is undergoing a massive transformation driven by artificial intelligence. But medical schools have barely started to teach about AI and machine learning -- creating knowledge gaps that could compound the damage caused by flawed algorithms and biased decision-support systems. "We're going to be at a point where we're not going to be able to catch up and be able to call out the technology defects or flaws," said Erkin Ötleş, a machine learning researcher working toward his medical degree and Ph.D. at the University of Michigan. "Without being armed with that set of foundational knowledge into how these things work, we're going to be at a disadvantage." In a recent commentary published in Cell Reports Medicine, Ötleş and a group of physicians and educators from the University of Michigan called for medical educators to make AI less of an afterthought and more of a core concept in undergraduate medical training.

Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data. In order to handle the heterogeneous label distribution across clients, most of the existing works only involve enforcing regularization between local and global models during optimization. In this paper, we fully utilize the models of all clients and propose a novel concept of \textit{major classifier vectors}, where a group of class vectors is obtained in an ensemble rather than the weighted average method on the server. The major classifier vectors are then distributed to all clients and the local training of each client is Contrasted with Major Classifier vectors (FedCMC), so the local model is not prone to overfitting to the local label distribution. FedCMC requires only a small amount of additional transfer of classifier parameters without any leakage of raw data, extracted representations, and label distributions. Our extensive experiments show that FedCMC outperforms the other state-of-the-art FL algorithms on three medical relation extraction datasets.

FIGURE 1: LMU seal (left) style-transferred to Van Gogh's Sunflower painting (center) and blended with the prompt - Van Gogh, sunflowers - via CLIP+VGAN (right). In the last few years, there have been several breakthroughs in the methodologies used in Natural Language Processing (NLP) as well as Computer Vision (CV). Beyond these improvements on single-modality models, large-scale multimodal approaches have become a very active area of research. In this seminar, we reviewed these approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other Chapter 3.1 and Chapter 3.2), as well as models in which one modality is utilized to enhance representation learning for the other (Chapter 3.3 and Chapter 3.4). To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced (Chapter 3.5). Finally, we also cover other modalities (Chapter 4.1 and Chapter 4.2) as well as general-purpose multi-modal models (Chapter 4.3), which are able to handle different tasks on different modalities within one unified architecture.

The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective. Due to the large architecture parameter space, efficiency is a key bottleneck preventing NAS from its practical use. In this paper, we address the issue by framing NAS as a multi-agent problem where agents control a subset of the network and coordinate to reach optimal architectures. We provide two distinct lightweight implementations, with reduced memory requirements (1/8th of state-of-the-art), and performances above those of much more computationally expensive methods. Theoretically, we demonstrate vanishing regrets of the form O(sqrt(T)), with T being the total number of rounds. Finally, aware that random search is an, often ignored, effective baseline we perform additional experiments on 3 alternative datasets and 2 network configurations, and achieve favourable results in comparison.

Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning. However, when dynamic motions under constraints are needed and computation time is limited, fast kinodynamic planning on the constraint manifold is indispensable. In recent years, learning-based solutions have become alternatives to classical approaches, but they still lack comprehensive handling of complex constraints, such as planning on a lower-dimensional manifold of the task space while considering the robot's dynamics. This paper introduces a novel learning-to-plan framework that exploits the concept of constraint manifold, including dynamics, and neural planning methods. Our approach generates plans satisfying an arbitrary set of constraints and computes them in a short constant time, namely the inference time of a neural network. This allows the robot to plan and replan reactively, making our approach suitable for dynamic environments. We validate our approach on two simulated tasks and in a demanding real-world scenario, where we use a Kuka LBR Iiwa 14 robotic arm to perform the hitting movement in robotic Air Hockey.

In a bid to run with the tide of digital economy, fresh facts have emerged that seven Deposit Money Banks (DMBs) are already competing in not only adoption but performance efficiency and customer satisfaction in the use of Artificial Intelligence-powered chatbots. The banks are Zenith Bank with chatbot called Ziva; Fidelity bank plc's Ivy; First City Monument Bank's Temi; UBA Group with its Leo, an AI-powered Facebook Messenger bot that allow users perform banking transactions; Access Bank's Tamada; Heritage Bank's octopus chatbot and Keystone Bank's chatbot called oxygen. A chatbot is a computer programme or an artificial intelligence which conducts a conversation through audio or text. They are messaging apps which allow businesses and brands to remain online 24 hours, providing customer support by instant responses and complaint resolution. AI, chatbots and automated, self-service technologies free up call centre employees from routine tier-1 support requests so they can focus on more complex tasks.

A dystopian future for cybersecurity is presented by all good apocalyptic sci-fi films and books. Beyond the inclusion of open AI in many vendor slide decks in a way of positioning next-generation technology and a more automated, advanced, approach to technology, the concept is now becoming more mainstream. This advancement from a glorified Google search has sparked the interest of students seeking to expedite essay responses without consuming the time or need to read source materials, followed by teachers seeking to similarly automate marking. Anyone who has been frustrated by a website chatbot while attempting to get help or an answer to a vaguely complex question found solace in conversing with a computer. Without a doubt, it's opened up a world of possibilities for AI to influence how we interact with technology daily as individuals, rather than just being a mysterious black box powering systems ranging from weather forecasting to space rockets. Inevitably, the potential impact on cyber security will quickly become a key topic of discussion on both sides.

The number of women authoring scientific papers is increasing, but men still dominate overall and some fields won't reach gender equity until the next century, according to a major analysis. Cassidy Sugimoto at the Georgia Institute of Technology in Atlanta and Vincent Larivière at the University of Montreal in Canada analysed nearly 5.5 million scientific papers published between 2008 and 2020, using a machine-learning algorithm to estimate the likelihood that a person's name belonged to a man or woman.

Male and female lar gibbons sing duets with notes that are synchronised and occur at regular intervals. These are rhythmic qualities similar to those found in human songs, which could hint at an evolutionary basis for the origins of music. "I'm pretty sure the gibbon's isochronous capacities are better than mine," says Andrea Ravignani at the Max Planck Institute for Psycholinguistics in the Netherlands, referring to the capacity to sing notes that occur at regularly repeating intervals. This ability has previously been noted in indris (Indri indri), a type of lemur found in Madagascar and the only other primate whose calls exhibit distinct rhythms related to those found in human music. Male and female gibbons regularly sing duets to define territory and form social bonds.