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.
Proposes MLCNN for better multivariate time series forecasting.
This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in vision and speech applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent vision and speech systems. With avai…
This research predicts stock market movements using Vision-Language models.
Quantum hybrid vision transformers improve event classification in high energy physics.
CVPR 2020 challenge evaluates continual learning in computer vision.
Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics. This is why pixel-space video prediction may be viewed as a promising avenue for unsupervised feature learn…
ViWi dataset framework tackles wireless communication problems with visual data.
Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification…
Survey of complex-valued neural networks for improved performance.
Evaluates deep learning models in histopathology for robustness and classification strategies.
Survey of self-supervised learning methods in computer vision, NLP, and graph learning.
Nowadays autonomous technologies are a very heavily explored area and particularly computer vision as the main component of vehicle perception. The quality of the whole vision system based on neural networks relies on the dataset it was trained on. It is extremely difficult to find traffic sign datasets from most of th…
A simple approach improves performance on both past and future tasks in lifelong learning.
Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have so…
Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution o…
The study investigates the impact of negative examples in contrastive learning.
We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end…
WebGUM learns web navigation from multimodal data, outperforming previous methods.
Survey of knowledge distillation for model compression.
Bayesian Neural Networks improve uncertainty reasoning in NNs.
Aligns text representations over time for better performance in sequential tasks.
Private learning needs more data or better features.
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in areas like data mining, computer…
CovidSens uses social media to track COVID-19 spread.
This paper reviews and proposes a unified framework for contrastive learning.
We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers an…
Autonomous Vehicles navigating in urban areas have a need to understand and predict future pedestrian behavior for safer navigation. This high level of situational awareness requires observing pedestrian behavior and extrapolating their positions to know future positions. While some work has been done in this field usi…
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research direction…
Sparse Vision MoE matches dense networks in image recognition while using less compute.
A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We firstly and briefly describe classical formulations of ML, and distinguish deterministic from statistical lear…
Robust CLIP improves vision models' resistance to attacks.
GANs generate images of emotions from datasets.
Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing s…
Robust PCA has drawn significant attention in the last decade due to its success in numerous application domains, ranging from bio-informatics, statistics, and machine learning to image and video processing in computer vision. Robust PCA and its variants such as sparse PCA and stable PCA can be formulated as optimizati…
Top 8 robotic vision systems tackled lifelong object recognition challenges.
Transformers improve time series modeling by capturing long-range dependencies.
Computer vision SSL methods show effectiveness on time series data.
CLIP models robustness to spurious features is re-evaluated using a new dataset.
Machine learning improves ice flow tracking in satellite images.
New dataset and benchmarks for lifelong robotic vision tasks.
Mobile V-MoEs scale down ViTs for resource-constrained vision tasks.
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural In…
GSA-Nets apply group equivariance to self-attention for vision tasks.
3DB framework tests and debugs computer vision models using photorealistic simulation.
This paper reviews methods for interpreting deep learning models with sequential data.
CViT learns complex physical systems using vision transformer techniques.
Computer vision models are unstable due to task symmetries and labelling issues.
We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-supervised manner. The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model …