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.
ARNe model excels in abstract visual reasoning tasks.
Constellation learns group-level visual relationships for abstract reasoning.
Improved few-shot visual reasoning with image preprocessing.
MXGNet tackles visual reasoning tasks using graph neural networks.
A new method learns object hierarchies from images to reason about physical interactions.
A disentangled representation encodes information about the salient factors of variation in the data independently. Although it is often argued that this representational format is useful in learning to solve many real-world down-stream tasks, there is little empirical evidence that supports this claim. In this paper, …
Program synthesis struggles with complex spatial relationships in image classification.
Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-s…
Modular RL modules solve complex 3D Sokoban tasks.
A framework isolates VQA reasoning from perception for better model evaluation.
Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Stand…
Few-shot visual reasoning model learns analogical relationships from small data.
Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various gener…
Develops models for temporally abstract reasoning and attention.
Deep learning models generate languages that lack abstract reasoning.
Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the…
CIB compresses variables causally, preserving key causal interactions.
Unified model learns concepts across domains like left and right.
We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - an…
Neural model predicts object states and physical parameters from visual observations.
The paper introduces a method to learn Markov state abstractions for reinforcement learning.
We introduce the new task of Acoustic Question Answering (AQA) to promote research in acoustic reasoning. The AQA task consists of analyzing an acoustic scene composed by a combination of elementary sounds and answering questions that relate the position and properties of these sounds. The kind of relational questions …
KINet learns object interactions without supervision for robotic pushing.
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…
Graph Neural Networks align with dynamic programming, improving algorithmic reasoning.
Agent learns causal relationships from visual data to perform tasks.
The abstract discusses how humans use visualizations in machine learning.
The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such r…
OP3 models entities for better task generalization in reinforcement learning.
System helps scientists visualize deep learning model of x-ray images.
Despite their impressive performance in many tasks, deep neural networks often struggle at relational reasoning. This has recently been remedied with the introduction of a plug-in relational module that considers relations between pairs of objects. Unfortunately, this is combinatorially expensive. In this extended abst…
We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations. Our approach, called Gradient-weighted Class Activation Mapping (Grad-CAM), uses class-specific gradient information to localize…
LatFormer improves geometric reasoning by incorporating lattice symmetry priors in attention mechanisms.
Agent Trading Arena trains LLMs in real-time financial markets to improve numerical reasoning.
In this work we explore the generalization characteristics of unsupervised representation learning by leveraging disentangled VAE's to learn a useful latent space on a set of relational reasoning problems derived from Raven Progressive Matrices. We show that the latent representations, learned by unsupervised training …
We are enveloped by stories of visual interpretations in our everyday lives. The way we narrate a story often comprises of two stages, which are, forming a central mind map of entities and then weaving a story around them. A contributing factor to coherence is not just basing the story on these entities but also, refer…
Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning a…
VTA learns hierarchical temporal structure for sequential data.
We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample…
A novel method for visual question answering using scene graphs and reinforcement learning.
Mid-training improves RL by identifying compact action abstractions.
Visualizes deep neural networks for speech recognition using learned topographic filter maps.
Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in many challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still missing the tools to analayze their performance and visualize the temporal abstractions t…
Enhances BO with expert preferences about abstract properties.
PrototypeML simplifies neural network design and development.
We describe a new method for visualizing topics, the distributions over terms that are automatically extracted from large text corpora using latent variable models. Our method finds significant -grams related to a topic, which are then used to help understand and interpret the underlying distribution. Compared with …
DPFRL uses particle filters for decision making with complex visual observations.
It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing pr…