Machine Learning

Machine Learning

 Neural Sketch Learning for Conditional Program Generation

We study the problem of generating source code in a strongly typed, Java-like programming language, given a label (for example a set of API calls or types) carrying a small amount of information about the code that is desired. The generated programs are expected to respect a “realistic”…


 Unsupervised Cipher Cracking Using Discrete GANs

 

This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of fid…


 Building a Conversational Agent Overnight with Dialogue Self-Play

We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, b…


 Improving Orbit Prediction Accuracy through Supervised Machine Learning

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satel…


 Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In …


 Predicting Movie Genres Based on Plot Summaries

  

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability …


 Eye-Movement behavior identification for AD diagnosis

 

In the present work, we develop a deep-learning approach for differentiating the eye-movement behavior of people with neurodegenerative diseases over healthy control subjects during reading well-defined sentences. We define an information compaction of the eye-tracking data of subjects without and …


 Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey

Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving…


 Full Wafer Redistribution and Wafer Embedding as Key Technologies for a Multi-Scale Neuromorphic Hardware Cluster

Together with the Kirchhoff-Institute for Physics(KIP) the Fraunhofer IZM has developed a full wafer redistribution and embedding technology as base for a large-scale neuromorphic hardware system. The paper will give an overview of the neuromorphic computing platform at the KIP and the associated h…


 tau-FPL: Tolerance-Constrained Learning in Linear Time

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because the…


 Sparsity-based Defense against Adversarial Attacks on Linear Classifiers

    

Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent work raises important questions about the robustness of such architectures, by showing that it is possible to induce class…


 Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by addin…


 Faster Deep Q-learning using Neural Episodic Control

  

The Research on deep reinforcement learning to estimate Q-value by deep learning has been active in recent years. In deep reinforcement learning, it is important to efficiently learn the experiences that a agent has collected by exploring the environment. In this research, we propose NEC2DQN that i…


 Top k Memory Candidates in Memory Networks for Common Sense Reasoning

Successful completion of reasoning task requires the agent to have relevant prior knowledge or some given context of the world dynamics. Usually, the information provided to the system for a reasoning task is just the query or some supporting story, which is often not enough for common reasoning ta…


 Crossmodal Attentive Skill Learner

  

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where th…


 Optimal Generalized Decision Trees via Integer Programming

Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if allowed to grow large, they lose interpretability. In this pa…


 Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis

 

Cell movement in the early phase of C. elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rule…


 Frame-Recurrent Video Super-Resolution

    

Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images. Current state-of-the-art methods process a batch of LR frames to generate…


 Deep Reinforcement Fuzzing

  

Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-t…


 Deep Portfolio Theory

We construct a deep portfolio theory. By building on Markowitz’s classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hie…


 Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

 

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequ…


 A Deep Incremental Boltzmann Machine for Modeling Context in Robots

Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltz…


 DCDistance: A Supervised Text Document Feature extraction based on class labels

Text Mining is a field that aims at extracting information from textual data. One of the challenges of such field of study comes from the pre-processing stage in which a vector (and structured) representation should be extracted from unstructured data. The common extraction creates large and sparse…


 Deep Learning in Finance

We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems — such as those presented in designing and pricing securities, constructing portfolios, and risk management — often involve large data sets with…


 Counterfactual equivalence for POMDPs, and underlying deterministic environments

Partially Observable Markov Decision Processes (POMDPs) are rich environments often used in machine learning. But the issue of information and causal structures in POMDPs has been relatively little studied. This paper presents the concepts of equivalent and counterfactually equivalent POMDPs, where…


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