Search Results for author:
Found 37 papers, 10 papers with code
A3T: Accuracy Aware Adversarial Training
Adversarial training has been empirically shown to be more prone to overfitting than standard training.
Interpretable Scientific Discovery with Symbolic Regression: A Review
Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data.
Impact of Adversarial Training on Robustness and Generalizability of Language Models
Specifically, we study the effect of pre-training data augmentation as well as training time input perturbations vs. embedding space perturbations on the robustness and generalization of BERT-like language models.
Ten Years after ImageNet: A 360° Perspective on AI
The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.
Offline Reinforcement Learning for Road Traffic Control
We build a model-based learning framework which infers a Markov Decision Process (MDP) from a dataset collected using a cyclic traffic signal control policy that is both commonplace and easy to gather.
Updating Street Maps using Changes Detected in Satellite Imagery
To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps.
Permutation-Invariant Subgraph Discovery
PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause.
Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories
Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in children and young adults.
Log representation as an interface for log processing applications
Log files are files that record events, messages, or transactions.
Probabilistic Outlier Detection and Generation
A new method for outlier detection and generation is introduced by lifting data into the space of probability distributions which are not analytically expressible, but from which samples can be drawn using a neural generator.
How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement
To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary.
Unravelling the Architecture of Membrane Proteins with Conditional Random Fields
In this paper, we will show that the recently introduced graphical model: Conditional Random Fields (CRF) provides a template to integrate micro-level information about biological entities into a mathematical model to understand their macro-level behavior.
Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
Inferring road graphs from satellite imagery is a challenging computer vision task.
RoadTagger: Robust Road Attribute Inference with Graph Neural Networks
The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers.
Inferring and Improving Street Maps with Data-Driven Automation
Through an evaluation on a large-scale dataset including satellite imagery, GPS trajectories, and ground-truth map data in forty cities, we show that Mapster makes automation practical for map editing, and enables the curation of map datasets that are more complete and up-to-date at less cost.
Machine-Assisted Map Editing
Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps.
AI-CARGO: A Data-Driven Air-Cargo Revenue Management System
The discrepancy results in sub-optimal and inefficient behavior by both the shipper and the airline resulting in the overall loss of potential revenue for the airline.
Risk Aware Ranking for Top-$k$ Recommendations
Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items.
Deep Learning for Anomaly Detection: A Survey
For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains.
Group Anomaly Detection using Deep Generative Models
Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points.
Anomaly Detection using One-Class Neural Networks
We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets.
RoadTracer: Automatic Extraction of Road Networks from Aerial Images
Mapping road networks is currently both expensive and labor-intensive.
Optimizing Non-decomposable Measures with Deep Networks
We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and non-decomposable.
SAGA: A Submodular Greedy Algorithm For Group Recommendation
In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users.
SPARK: Static Program Analysis Reasoning and Retrieving Knowledge
In this work, we present a machine learning pipeline that induces a security analyzer for programs by example.
Robust, Deep and Inductive Anomaly Detection
PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique.
Kharita: Robust Map Inference using Graph Spanners
Our algorithms utilize techniques from graph spanners so that they produce maps can effectively handle a wide variety of road and intersection shapes.
Other Computer Science
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting
An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date.
Online Optimization Methods for the Quantification Problem
The estimation of class prevalence, i. e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc.
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation
Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems.
Parameter Database : Data-centric Synchronization for Scalable Machine Learning
We propose a new data-centric synchronization framework for carrying out of machine learning (ML) tasks in a distributed environment.
On Integrated Clustering and Outlier Detection
We model the joint clustering and outlier detection problem using an extension of the facility location formulation.
Classification of Passes in Football Matches using Spatiotemporal Data
Experimental results show that we are able to produce a classifier with 85. 8% accuracy on classifying passes as Good, OK or Bad, and that the predictor variables computed using complex methods from computational geometry are of moderate importance to the learned classifiers.
Integer Programming Relaxations for Integrated Clustering and Outlier Detection
We present and contrast three relaxations to the integer program formulation: (i) a linear programming formulation (LP) (ii) an extension of affinity propagation to outlier detection (APOC) and (iii) a Lagrangian duality based formulation (LD).
Network Traffic Decomposition for Anomaly Detection
In this paper we focus on the detection of network anomalies like Denial of Service (DoS) attacks and port scans in a unified manner.
Sleep Analytics and Online Selective Anomaly Detection
We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to model a specific scenario emerging from research in sleep science.
Feature Graph Architectures
In this article we propose feature graph architectures (FGA), which are deep learning systems employing a structured initialisation and training method based on a feature graph which facilitates improved generalisation performance compared with a standard shallow architecture.
