3 research outputs found
Neural Program Synthesis By Self-Learning
Neural inductive program synthesis is a task generating instructions that can
produce desired outputs from given inputs. In this paper, we focus on the
generation of a chunk of assembly code that can be executed to match a state
change inside the CPU and RAM. We develop a neural program synthesis algorithm,
AutoAssemblet, learned via self-learning reinforcement learning that explores
the large code space efficiently. Policy networks and value networks are
learned to reduce the breadth and depth of the Monte Carlo Tree Search,
resulting in better synthesis performance. We also propose an effective
multi-entropy policy sampling technique to alleviate online update
correlations. We apply AutoAssemblet to basic programming tasks and show
significant higher success rates compared to several competing baselines
Rethinking Exposure Bias In Language Modeling
Exposure bias describes the phenomenon that a language model trained under
the teacher forcing schema may perform poorly at the inference stage when its
predictions are conditioned on its previous predictions unseen from the
training corpus. Recently, several generative adversarial networks (GANs) and
reinforcement learning (RL) methods have been introduced to alleviate this
problem. Nonetheless, a common issue in RL and GANs training is the sparsity of
reward signals. In this paper, we adopt two simple strategies, multi-range
reinforcing, and multi-entropy sampling, to amplify and denoise the reward
signal. Our model produces an improvement over competing models with regards to
BLEU scores and road exam, a new metric we designed to measure the robustness
against exposure bias in language models
Type-driven Neural Programming by Example
In this thesis we look into programming by example (PBE), which is about
finding a program mapping given inputs to given outputs. PBE has traditionally
seen a split between formal versus neural approaches, where formal approaches
typically involve deductive techniques such as SAT solvers and types, while the
neural approaches involve training on sample input-outputs with their
corresponding program, typically using sequence-based machine learning
techniques such as LSTMs [41]. As a result of this split, programming types had
yet to be used in neural program synthesis techniques.
We propose a way to incorporate programming types into a neural program
synthesis approach for PBE. We introduce the Typed Neuro-Symbolic Program
Synthesis (TNSPS) method based on this idea, and test it in the functional
programming context to empirically verify type information may help improve
generalization in neural synthesizers on limited-size datasets.
Our TNSPS model builds upon the existing Neuro-Symbolic Program Synthesis
(NSPS), a tree-based neural synthesizer combining info from input-output
examples plus the current program, by further exposing information on types of
those input-output examples, of the grammar production rules, as well as of the
hole that we wish to expand in the program.
We further explain how we generated a dataset within our domain, which uses a
limited subset of Haskell as the synthesis language. Finally we discuss several
topics of interest that may help take these ideas further. For reproducibility,
we release our code publicly