Computer Science and Artificial Intelligence Lab (CSAIL)
http://hdl.handle.net/1721.1/5458
2018-10-19T18:28:49ZTowards Understanding Generalization via Analytical Learning Theory
http://hdl.handle.net/1721.1/118307
Towards Understanding Generalization via Analytical Learning Theory
Kawaguchi, Kenji; Benigo, Yoshua; Verma, Vikas; Kaelbling, Leslie Pack
This paper introduces a novel measure-theoretic theory for machine learning
that does not require statistical assumptions. Based on this theory, a new
regularization method in deep learning is derived and shown to outperform
previous methods in CIFAR-10, CIFAR-100, and SVHN. Moreover, the proposed
theory provides a theoretical basis for a family of practically successful
regularization methods in deep learning. We discuss several consequences of
our results on one-shot learning, representation learning, deep learning,
and curriculum learning. Unlike statistical learning theory, the proposed
learning theory analyzes each problem instance individually via measure
theory, rather than a set of problem instances via statistics. As a result,
it provides different types of results and insights when compared to
statistical learning theory.
2018-10-01T00:00:00ZUsing Dynamic Monitoring to Synthesize Models of Applications That Access Databases
http://hdl.handle.net/1721.1/118184
Using Dynamic Monitoring to Synthesize Models of Applications That Access Databases
Shen, Jiasi; Rinard, MArtin
We previously developed Konure, a tool that uses active learning to
infer the functionality of database applications. An alternative
approach is to observe the inputs, outputs, and database traffic from a
running system in normal use and then synthesize a model of the
application from this information. To evaluate these two approaches, we
present Etch, which uses information from typical usage scenarios to
synthesize a model of the functionality of database applications whose
computation can be expressed in the Konure DSL.
2018-09-27T00:00:00ZUsing Active Learning to Synthesize Models of Applications That Access Databases
http://hdl.handle.net/1721.1/117593
Using Active Learning to Synthesize Models of Applications That Access Databases
Shen, Jiasi; Rinard, Martin
We present a new technique that uses active learning to infer models of
applications that manipulate relational databases. This technique
comprises a domain-specific language for modeling applications that
access databases (each model is a program in this language) and an
associated inference algorithm that infers models of applications whose
behavior can be expressed in this language. The inference algorithm
generates test inputs and database configurations, runs the application,
then observes the resulting database traffic and outputs to
progressively refine its current model hypothesis. The end result is a
model that completely captures the behavior of the application. Because
the technique works only with the externally observable inputs, outputs,
and databases, it can infer the behavior of applications written in
arbitrary languages using arbitrary coding styles (as long as the
behavior of the application is expressible in the domain-specific language).
We also present a technique for automatically regenerating an
implementation from the inferred model. The regenerator can produce a
translated implementation in a different language and systematically
include relevant security and error checks.
2018-08-28T00:00:00ZData and Code for "A New Approach to Animacy Detection"
http://hdl.handle.net/1721.1/116172
Data and Code for "A New Approach to Animacy Detection"
Labiba, Jahan,; Geeticka, Chauhan,; A., Finlayson, Mark
This archive contains the code and data for the workshop article "A New Approach to Animacy Detection," published in 2018 in the the 27th International Conference on Computational Linguistics (COLING 2018), in Santa Fe, NM. The root of the archive contains a readme file which explains the archive contents. Furthermore, the archive can be imported directly into the Eclipse IDE as a project encapsulating the executable code and data required to reproduce the results of the paper; the code compiles with Java 1.8. The archive also contains a copy of the near-final version of the paper for reference.
2018-06-07T00:00:00Z