Search Results for author:
Found 13 papers, 3 papers with code
DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms
DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms.
Deriving Weeklong Activity-Travel Dairy from Google Location History: Survey Tool Development and A Field Test in Toronto
This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis.
Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics
This study developed models to predict household' weekly home delivery frequencies.
Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and Tornado Cash
Mixers, such as Tornado Cash, were developed to preserve privacy through "mixing" transactions with those of others in an anonymity pool, making it harder to link deposits and withdrawals from the pool.
Towards Human-Understandable Visual Explanations: Human Imperceptible Cues Can Better Be Removed
Explainable AI (XAI) methods focus on explaining what a neural network has learned - in other words, identifying the features that are the most influential to the prediction.
MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization
One of the most common problems of weakly supervised object localization is that of inaccurate object coverage.
Towards Human-Understandable Visual Explanations:Imperceptible High-frequency Cues Can Better Be Removed
Explainable AI (XAI) methods focus on explaining what a neural network has learned - in other words, identifying the features that are the most influential to the prediction.
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing
Given a really low-resolution input image of a face (say 16x16 or 8x8 pixels), the goal of this paper is to reconstruct a high-resolution version thereof.
Information Compensation for Deep Conditional Generative Networks
In recent years, unsupervised/weakly-supervised conditional generative adversarial networks (GANs) have achieved many successes on the task of modeling and generating data.
In Defense of LSTMs for Addressing Multiple Instance Learning Problems
LSTMs have a proven track record in analyzing sequential data.
An Unpaired Shape Transforming Method for Image Translation and Cross-Domain Retrieval
We address the problem of unpaired geometric image-to-image translation.
Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
In this paper, we propose a novel scheme for both interpretation as well as explanation in which, given a pretrained model, we automatically identify internal features relevant for the set of classes considered by the model, without relying on additional annotations.
An Analysis of Human-centered Geolocation
Experiments on the Fashion 144k and a Pinterest-based dataset show that the automatic methods succeed at this task to a reasonable extent.
