arXiv research

A new approach for specifying and synthesizing subroutines for optimizing metrics.

This paper evaluates how well neural models can solve complex tasks by breaking them into simpler ones.

Neural networks struggle with long sequences, but a new method improves their performance.

Method estimates dataset utility via minimal program length proxy.

Reverse annealing boosts quantum matrix factorization performance.

Quantum algorithm speeds up Lasso regression by quadratically faster per iteration.

Tree-based synthesis improves forecast accuracy in GDP and inflation.

Machine learning algorithms typically rely on optimization subroutines and are well-known to provide very effective outcomes for many types of problems. Here, we flip the reliance and ask the reverse question: can machine learning algorithms lead to more effective outcomes for optimization problems? Our goal is to trai…

Speech synthesis from EEG features using RNN.

SED integrates synthesis, execution, and debugging for neural program synthesis.

The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process --- creating audio samples from a score and instrument information --- is modeled using generative neural networks. This paper describes a neural music synthesis model with fl…

System uses machine learning and automated reasoning to speed up PBE synthesis.

Study shows effectiveness of offline RL in online RL tasks.

The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative met…

A neural program synthesis method with iterative fix operations.

New approach generates better synthetic data for neural program synthesis.

We prove that spherical spectral analysis and synthesis hold in Damek-Ricci spaces and derive two-radius theorems.

Inverse Drum Machine separates drum mixes using transcription and synthesis.

New algorithm achieves optimal regret in average reward MDPs without prior bias information.

Language models predict inorganic synthesis conditions and temperatures.

CrossBeam learns to search more efficiently in program synthesis.

We propose a framework for synthesis of geological images based on an exemplar image. We synthesize new realizations such that the discrepancy in the patch distribution between the realizations and the exemplar image is minimized. Such discrepancy is quantified using a kernel method for two-sample test called maximum m…

BOiLS optimizes circuit quality using Bayesian optimization.

AutoDiff combines auto-encoder and diffusion model for realistic tabular data synthesis.

Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes. We assume pixel-wise segmentation labels are available during training and us…

Diffusion models outperform GANs in image synthesis quality.

Develops BPDS for better financial portfolio decisions.

The conjugate gradient (CG) method is an efficient iterative method for solving large-scale strongly convex quadratic programming (QP). In this paper we propose some generalized CG (GCG) methods for solving the $\ell_1$-regularized (possibly not strongly) convex QP that terminate at an optimal solution in a finite numb…

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable \emph{T…

Speech-driven visual speech synthesis involves mapping features extracted from acoustic speech to the corresponding lip animation controls for a face model. This mapping can take many forms, but a powerful approach is to use deep neural networks (DNNs). However, a limitation is the lack of synchronized audio, video, an…

WaveCycleGAN has recently been proposed to bridge the gap between natural and synthesized speech waveforms in statistical parametric speech synthesis and provides fast inference with a moving average model rather than an autoregressive model and high-quality speech synthesis with the adversarial training. However, the …

Model for directed synthesis of audio textures using multi-scale RNNs.

We present a program synthesis-oriented dataset consisting of human written problem statements and solutions for these problems. The problem statements were collected via crowdsourcing and the program solutions were extracted from human-written solutions in programming competitions, accompanied by input/output examples…

Proposes a synthesis algorithm using Conformal Prediction for improved Deep Learning performance.

This paper explores optimising acquisition functions in Bayesian optimisation.

InVAErt networks use data-driven methods for system synthesis and identifiability analysis.

Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector …

We propose a framework that directly tackles the probability distribution of the value function parameters in Deep Q Network (DQN), with powerful variational inference subroutines to approximate the posterior of the parameters. We will establish the equivalence between our proposed surrogate objective and variational i…

This paper improves speech synthesis using a DGP with SRU for naturalness.

The study examines optimal synthesis in a radially symmetric Grushin space with conditions on the weight function.

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural la…

New methodology controls synthetic data bias for neural program synthesis.

Bayesian spatial predictive synthesis improves spatial data predictions.

Principles of financial product synthesis from a few basic financial products constitute an interesting research topic inspired by Islamic finance. We make an effort to answer general questions that should be answered before starting to investigate the main issues concerning this topic with the formalization of financi…

Previous work in hierarchical reinforcement learning has faced a dilemma: either ignore the values of different possible exit states from a subroutine, thereby risking suboptimal behavior, or represent those values explicitly thereby incurring a possibly large representation cost because exit values refer to nonlocal a…

Improved texture synthesis using wavelet-based statistics with rectifier non-linearity.

Generative Adversarial Networks have been crucial in the developments made in unsupervised learning in recent times. Exemplars of image synthesis from text or other images, these networks have shown remarkable improvements over conventional methods in terms of performance. Trained on the adversarial training philosophy…

End-to-end speech synthesis is a promising approach that directly converts raw text to speech. Although it was shown that Tacotron2 outperforms classical pipeline systems with regards to naturalness in English, its applicability to other languages is still unknown. Japanese could be one of the most difficult languages …