Worldsheet wraps a 3D mesh sheet onto a single image to synthesize novel views.
problem Synthesizing novel views from a single image with large viewpoint changes.
method Shrink-wrapping a planar mesh sheet onto the input image, consistent with learned depth.
result Worldsheet consistently outperforms prior methods on single-image view synthesis.
We integrate camera pose correlations into deep models using Gaussian processes.
problem Lack of inter-frame reasoning in deep neural networks.
method Derive a principled framework combining camera pose information with deep models using a novel view kernel.
result Soft-prior knowledge aids pose-related vision tasks like novel view synthesis.
Improved texture synthesis using wavelet-based statistics with rectifier non-linearity.
problem Improving texture synthesis quality using wavelet representations.
method Proposes a family of statistics based on non-linear wavelet representations with a generalized rectifier non-linearity.
result Significantly improves visual quality of texture synthesis compared to classical wavelet-based models.
StyleNeRF generates high-resolution images with 3D consistency and style control.
problem Generating high-resolution images with fine details and 3D consistency.
method Integrates NeRF into a style-based generator for efficient high-resolution image synthesis.
result Synthesizes high-resolution images at interactive rates with high 3D consistency and style control.
This study shows how EEG can be used to generate fMRI data.
problem Mapping fMRI from EEG signals.
method Deep learning approaches (Autoencoders, GANs, Pairwise Learning).
result Feasibility of EEG to fMRI brain image mappings.
Direct Feedback Alignment performs well on diverse deep learning tasks and architectures.
problem The limitations of backpropagation in parallelizing and scaling to modern deep learning tasks.
method Direct Feedback Alignment approach applied to neural view synthesis, recommender systems, geometric learning, and natural language processing.
result Direct Feedback Alignment successfully trains a wide range of state-of-the-art deep learning architectures with performance close to fine-tuned backpropagation.
DreamFusion uses text-to-image diffusion models to create 3D images efficiently.
problem Lack of large-scale 3D datasets and efficient architectures for 3D synthesis.
method Adapting a 2D diffusion model to 3D synthesis using a loss based on probability density distillation.
result A 3D model can be optimized from a 2D diffusion model, allowing for text-to-3D synthesis.
Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis. In the traditional Gaussian process regression model, the kernel functions are defined explicitly. The computational complexity of training is O(N 3 ), and the computation complexity of prediction i…
GTA improves transformer-based NVS models by encoding geometric structure.
problem Suboptimal positional encoding for 3D vision tasks.
method Geometry-aware attention mechanism encoding geometric structure of tokens.
result GTA improves learning efficiency and performance of NVS models.
Tree-based synthesis improves forecast accuracy in GDP and inflation.
problem Improving forecast accuracy in GDP and inflation.
method Developed a nonparametric synthesis function using regression trees.
result Tree-based synthesis leads to improved forecast accuracy.
Speech synthesis from EEG features using RNN.
problem Speech synthesis from EEG data.
method Recurrent Neural Network (RNN) regression model to predict acoustic features from EEG features.
result Feasibility of synthesizing speech directly from EEG features demonstrated.
SED integrates synthesis, execution, and debugging for neural program synthesis.
problem Challenges in synthesizing complex programs that match specifications.
method SED combines synthesis, execution, and debugging to improve neural program generation.
result SED reduces error rates and outperforms standard decoding methods.
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.
problem Slow synthesis in PBE due to domain-specific knowledge and large training datasets.
method Preprocess SyGuS PBE problems with a neural network to reduce search space, then use automated reasoning for faster solution.
result System outperforms all competing tools in the 2019 SyGuS Competition for the PBE Strings track by 47.65%.
Scientific investigations that incorporate next generation sequencing involve analyses of high-dimensional data where the need to organize, collate and interpret the outcomes are pressingly important. Currently, data can be collected at the microbiome level leading to the possibility of personalized medicine whereby tr…
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.
problem Creating correct programs from input-output examples.
method Combines encoder-decoder synthesis with a differentiable fixer.
result Improves synthesis accuracy by reducing discrepancies between outputs and desired outputs.
New approach generates better synthetic data for neural program synthesis.
problem Current approaches to neural program synthesis generalize poorly to real data.
method Adversarial approach to control synthetic data distributions.
result Proposed method outperforms current approaches.
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.
problem Separating individual drum tracks from mixed recordings.
method Analysis-by-synthesis framework combining deep learning and automatic transcription.
result Separation quality comparable to supervised methods requiring isolated stems.
Language models predict inorganic synthesis conditions and temperatures.
problem Limited data and heuristic approaches constrain inorganic synthesis planning.
method Language models without fine-tuning predict precursor conditions and temperatures.
result Language models achieve high accuracy in predicting synthesis conditions and temperatures.
CrossBeam learns to search more efficiently in program synthesis.
problem Efficiently searching through vast program spaces.
method Trains a neural model to guide program synthesis, combining previously explored programs.
result CrossBeam explores much smaller portions of the program space compared to state-of-the-art methods.
The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of Deep Dream-based convolutional neural n…
VERA-V uses variational inference to discover vulnerabilities in multimodal vision-language models.
problem Existing methods for jailbreaking vision-language models are brittle, limited, and focus on single attacks.
method VERA-V recasts jailbreak discovery as learning a joint posterior distribution over text-image prompts, using variational inference and three complementary strategies.
result VERA-V consistently outperforms state-of-the-art baselines, achieving up to 53.75% higher attack success rate.
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.
problem Optimizing circuits with complex search spaces.
method Adapting Bayesian optimization to logic synthesis, using Gaussian process kernels and trust-region constrained acquisitions.
result Demonstrated superior performance in sample efficiency and QoR values.
AutoDiff combines auto-encoder and diffusion model for realistic tabular data synthesis.
problem Generating realistic synthetic tabular data with heterogeneous features.
method Employing auto-encoder architecture to handle tabular data's complexity.
result Synthetic tables from AutoDiff show good statistical fidelity and perform well in machine learning tasks.
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…
We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for such poli…
Diffusion models outperform GANs in image synthesis quality.
problem Improving image synthesis quality using generative models.
method Developed and optimized diffusion models, and introduced classifier guidance for conditional synthesis.
result Achieved superior image synthesis quality with diffusion models, matching or exceeding GANs in FID scores.
Develops BPDS for better financial portfolio decisions.
problem Model uncertainty in financial time series forecasting.
method Bayesian dynamic modelling and predictive decision synthesis.
result Improved predictive and decision outcomes compared to traditional Bayesian analysis.
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.
problem Challenges in modeling complex audio textures with traditional methods.
method Combining multi-scale RNNs with a conditioning strategy for user-directed synthesis.
result Demonstrated improved performance on various audio texture datasets.
BPI models 2D patterns on multiple planes and 3D scene from a single image.
problem Understanding and editing images with multiple 2D planes and 3D scene from a single image.
method Box Program Induction (BPI) with neural networks and search-based algorithm.
result Holistic, structured scene representation enables 3D-aware image editing.
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.
problem Assessing the quality of synthesised data for high-stake domains.
method Conformal Prediction framework for generating data from high-confidence feature space regions.
result Training sets extended with confident synthesised data improved Deep Learning performance by up to 61 percentage points F1-score.
InVAErt networks use data-driven methods for system synthesis and identifiability analysis.
problem Model synthesis and identifiability analysis for complex systems.
method Deterministic encoder and decoder, normalizing flow, variational encoder, loss function penalty coefficients, latent space sampling.
result Validation through various system types, demonstrating effectiveness of the framework.
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 …
Hybrid model learns novel handwritten characters better than neural or symbolic models alone.
problem Generating novel yet structured concepts.
method Neuro-symbolic model combining neural networks and probabilistic programs.
result Hybrid model outperforms alternative models in learning and generalizing novel handwritten characters.
Method synthesizes 4D CMR images from XCAT model using GAN and SPADE.
problem Synthesizing realistic 4D CMR images with annotations and adaptable styles.
method Hybrid GAN approach with XCAT anatomical ground truth and SPADE for semantic preservation.
result Synthesized images with modality-specific features learned from real CMR data.
This paper improves speech synthesis using a DGP with SRU for naturalness.
problem Improving naturalness in synthetic speech.
method Deep Gaussian process with a recurrent architecture using SRU.
result SRU-DGP outperforms other models in naturalness of synthetic speech.
The study examines optimal synthesis in a radially symmetric Grushin space with conditions on the weight function.
problem Optimal synthesis in a radially symmetric Grushin space with a weight function.
method Analysis of the geometry of R3 with a weighted Carnot-Carathéodory metric, providing conditions for Grushin-like structure, and describing optimal synthesis. result Sufficient conditions on the weight function ensure a Grushin-like structure, and the candidate cut time coincides with the true cut time in the integrable case.
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.
problem Deep networks generalize poorly to certain data distributions when trained on synthetic examples.
method Proposes a new methodology to control and evaluate the bias of synthetic data distributions over programs and specifications.
result Training deep networks on controlled synthetic data distributions leads to improved cross-distribution generalization performance.
Bayesian spatial predictive synthesis improves spatial data predictions.
problem Model misspecification and heterogeneity in spatial data.
method Bayesian ensemble methodology capturing spatially-varying model uncertainty and performance heterogeneity.
result Synthesized predictions outperform standard methods in accuracy and uncertainty quantification.
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…
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…