A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Visualizes deep generative models for drug design.
The abstract discusses how humans use visualizations in machine learning.
PySS3 simplifies access to SS3's text classification and visualization.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
New RL environments help AI learn causal relationships from visual data.
Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activities are designed with deep neural networks (DNNs), but DNNs do not provide uncertainty estimates. Capturing reliable uncertainty estimates i…
AI-enhanced product embeddings boost demand analysis accuracy.
This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching c…
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the …
Research tackles distribution shift issues in ML to improve AI reliability.
ViCE uses superpixels to enhance self-supervised learning for better dense visual embeddings.
More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems. These problems are now known as Bongard problems. Although they are well known in the cognitive science and AI communities only moderate progress has been made towards building systems that can so…
CDPs visualize causal dependencies in AI models.
AI measures financial risk using linear quantile lasso regression.
Dp-CLIP preserves privacy in multimodal AI training.
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning disentangled representations of the underlying factors of variation. We draw inspiration from neuroscience, and show how this can be achiev…
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all pa…
Study copyright's impact on creative industries using AI-generated fonts.
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid …
Enhanced visual feature attribution via adaptive baseline weighting.
Improves AI agents' 3D navigation by learning from failures and 3D spatial relationships.
Proposes manifold-based unsupervised anomaly detection for visual data.
Visual Question Answering (VQA) requires AI models to comprehend data in two domains, vision and text. Current state-of-the-art models use learned attention mechanisms to extract relevant information from the input domains to answer a certain question. Thus, robust attention mechanisms are essential for powerful VQA mo…
Improved AI lung ultrasound segmentation using expert confidence values.
VisRuler simplifies decision extraction from bagged and boosted trees.
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement lear…
LightGCNet simplifies AI for soft sensors, reducing complexity and training time.
Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to p…
AI enhances pollen recognition in veterinary imaging using holographic microscopy.
Deep learning predicts ICU mortality with enhanced interpretability.
New text-to-image diffusion models improve scene understanding for AI agents.
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data. In order to evaluate and analyse the architecture, we introduce a family of simp…
Sparse DNNs face scalability issues; MIT/IEEE/Amazon challenge analyzes best solutions.
This paper rates robustness of multi-modal time-series forecasting models.
Researchers compute the spectrum of Hodge-Laplacian on 1-forms for SU(2) and SO(3).
This paper presents the first two editions of Visual Doom AI Competition, held in 2016 and 2017. The challenge was to create bots that compete in a multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots had to make their decisions based solely on visual information, i.e., a raw screen buffer. To p…
With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment ana…
FinAgent tackles financial trading with multimodal data and advanced AI.
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The proposed Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performanc…
AI stocks hedge against AI singularity's economic impact.
Enhances crowd safety through AI and data-driven models.
Despite the growing popularity of modern machine learning techniques (e.g. Deep Neural Networks) in cyber-security applications, most of these models are perceived as a black-box for the user. Adversarial machine learning offers an approach to increase our understanding of these models. In this paper we present an appr…
Study analyzes AI's impact on firms, markets, and workers using large language model data.
New AI stock indices classify firms' AI engagement using 10-K filings.
Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end up focusing on simple utterances and suboptimal policies. To mitigate this problem, we propose a class of novel temperature-based extension…
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
This review covers AI in finance, challenges, techniques, and opportunities.