Collision detection is one of the most time-consuming operations during motion planning. Thus, there is an increasing interest in exploring machine learning techniques to speed up collision detection and sampling-based motion planning. A recent line...
Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output. To answer a broader set of questions, we generalize a popular, mathematically well-grounded explanation technique, Shapley A...
Information leakage issues in machine learning-based Web applications have attracted increasing attention. While the risk of data privacy leakage has been rigorously analyzed, the theory of model function leakage, known as Model Extraction Attacks (M...
As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially critical in a manufacturing setting where smart factories rely on mac...
In many machine learning applications, there are multiple decision-makers involved, both automated and human. The interaction between these agents often goes unaddressed in algorithmic development. In this work, we explore a simple version of this in...
As machine learning models become more capable, they have exhibited increased potential in solving complex tasks. One of the most promising directions uses deep reinforcement learning to train autonomous agents in computer network defense tasks. This...
Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled approach for estimating broad classes of such models, including probabilis...
separate shapes of the right and left feet. The development of an automated lasting machine by the Surinamese-American Jan Ernst Matzeliger in the 1880s was
Well, P700 is a powerful machine and the only thing is lacking is TPM 2.0 update. We can find TPM 2.0 update, dated 25 Oct 2017 for the machines like P410, P510, P710. So I was hoping why not P700 as …
Neural Module Network (NMN) is a machine learning model for solving the visual question answering tasks. NMN uses programs to encode modules' structures, and its modularized architecture enables it to solve logical problems more reasonably. However,...
Alluxio, data orchestration for analytics and machine learning in the cloud (⭐ 7164)
Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictabi...
? Research implementation of W-5 Multi-Agent AI Consensus Framework for football match outcome prediction | AI-powered sports analytics using LLMs + Machine Learning | 85.9% accuracy (⭐ 12)
GalvanizeU Machine Learning & Natural Language Processing Final Project. Explored topic modeling approaches in Python to summarize emails by latent foreign policy topics using Non-Negative Matrix Factorization and LDA approaches in Sklearn (⭐ 13)
Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a biased manner that gives undue advantages to a specific group of people...
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There are three new arrivals from different categories: the first is an unstoppable mutant, isn't it? It's said that once he starts his machine, no one can stop the juggernaut! The second is a vigilan...
Machine learning (ML) models are increasingly deployed to production, calling for efficient inference serving systems. Efficient inference serving is complicated by two challenges: (i) ML models incur high computational costs, and (ii) the request ar...
Classical and contemporary distributed consensus protocols, may they be for binary agreement, state machine replication, or blockchain consensus, require all protocol participants in a peer-to-peer system to agree on exactly the same information as p...
Symmetry is fundamental to understanding physical systems and can improve performance and sample efficiency in machine learning. Both pursuits require knowledge of the underlying symmetries in data, yet discovering these symmetries automatically is c...