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.
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
Machine learning and C-NLP improve emergency department triage accuracy.
Deep learning for COVID-19 diagnosis using CXR images with limited data.
Gradient-based algorithm improves model performance with triage.
PCA-Triage optimizes sensor data sampling for IoT networks.
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We per…
Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual …
Agents learn to communicate and solve navigation tasks efficiently.
SpeakerStew verifies 46 languages with reduced training and inference costs.
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of E…
Deep learning model predicts severe COVID-19 outcomes.
Study assesses CNN model robustness to noise in low-cost CT scans.
Email has remained a principal form of communication among people, both in enterprise and social settings. With a deluge of emails crowding our mailboxes daily, there is a dire need of smart email systems that can recover important emails and make personalized recommendations. In this work, we study the problem of pred…
Study predicts room occupancy using machine learning, achieving high accuracy.
Bayesian neural networks improve SHD classification and uncertainty quantification.
We tune one of the most common heating, ventilation, and air conditioning (HVAC) control loops, namely the temperature control of a room. For economical and environmental reasons, it is of prime importance to optimize the performance of this system. Buildings account from 20 to 40% of a country energy consumption, and …
With an aging and growing population, the number of women requiring either screening or symptomatic mammograms is increasing. To reduce the number of mammograms that need to be read by a radiologist while keeping the diagnostic accuracy the same or better than current clinical practice, we develop Man and Machine Mammo…
Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging becaus…
New method considers subjectivity in text analysis using 'Room Theory'.
Autoencoder neural networks reconstruct missing indoor environment data.
Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US. These data are often r…
Paper tackles robust offline RL with heavy-tailed rewards.
CSAC enables cooperative reinforcement learning for multi-stage tasks.
Quantum computing at room temperature achieves high accuracy in image classification.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
The paper optimizes air conditioning setpoints using machine learning.
Bing's house-like spines approximate all PL manifolds.
Deep neural networks predict B-cell epitopes for SARS-CoV and SARS-CoV-2.
We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objec…
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and sol…
Study on packing links with geometric constraints.
Clinical notes are a rich source of information about patient state. However, using them to predict clinical events with machine learning models is challenging. They are very high dimensional, sparse and have complex structure. Furthermore, training data is often scarce because it is expensive to obtain reliable labels…
In this work we explore the use of metric index structures, which accelerate nearest neighbor queries, in the scenario where we need to interleave insertions and queries during deployment. This use-case is inspired by a real-life need in malware analysis triage, and is surprisingly understudied. Existing literature ten…
OtoWorld helps agents learn to navigate by listening in interactive environments.
Paper presents a method to detect out-of-distribution spectra in intra-operative functional imaging.
New method predicts and optimizes test-time scaling for LLMs.
New insights into state representations in RL help design better learning rules.
PriceAggregator optimizes hotel price fetching to increase Agoda's bookings.
Reconstructs graph structure from noisy data samples.
EUREKA builds classifiers that use surprising features.
Ray marching method visualizes flat surfaces efficiently.
For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In pa…
We present a program synthesis-oriented dataset consisting of human written problem statements and solutions for these problems. The problem statements were collected via crowdsourcing and the program solutions were extracted from human-written solutions in programming competitions, accompanied by input/output examples…
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point of care and time to treat. We use an attention-based convolutional neural netwo…
While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the compositional and combinatorial nature that makes them notoriously hard to optimize. With the emerging field of Text-Based Games (TBGs), re…
We study the fundamental limits to communication-efficient distributed methods for convex learning and optimization, under different assumptions on the information available to individual machines, and the types of functions considered. We identify cases where existing algorithms are already worst-case optimal, as well…
Quantum kernel machines need to use more complex kernels to fully exploit their potential.
DDSP integrates signal processing with deep learning for high-fidelity audio synthesis.