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Found 14 papers, 1 papers with code
Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies
This paper describes Amazon Alexa AI’s implementation for the IWSLT 2022 shared task on formality control.
On the Societal Impact of Open Foundation Models
To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk.
CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation
Given a trained model and a subset of training data designated to be forgotten (i. e., the "forget set"), we introduce a three-step process, named CovarNav, to facilitate this forgetting.
The Foundation Model Transparency Index
We score 10 major foundation model developers (e. g. OpenAI, Google, Meta) against the 100 indicators to assess their transparency.
Development of a Neural Network-based Method for Improved Imputation of Missing Values in Time Series Data by Repurposing DataWig
Unlike the original DataWig, tsDataWig can directly handle values of time variables and impute missing values in complex time series datasets.
A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System
This paper introduces a system used to generate game feature suggestions based on a text prompt.
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification
In this study, we explore the feasibility of leveraging federated learning for privacy-preserving training of deep neural networks for phytoplankton classification.
Measuring and Clustering Network Attackers using Medium-Interaction Honeypots
Network honeypots are often used by information security teams to measure the threat landscape in order to secure their networks.
Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping
In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area.
An Online Reinforcement Learning Approach to Quality-Cost-Aware Task Allocation for Multi-Attribute Social Sensing
Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world.
Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos.
Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences
Performing arts organizations aim to enrich their communities through the arts.
