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Found 26 papers, 12 papers with code
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions.
Flora: Low-Rank Adapters Are Secretly Gradient Compressors
Despite large neural networks demonstrating remarkable abilities to complete different tasks, they require excessive memory usage to store the optimization states for training.
Ginger: An Efficient Curvature Approximation with Linear Complexity for General Neural Networks
The major reason is due to the quadratic memory and cubic time complexity to compute the inverse of the matrix.
Ensemble Distillation for Unsupervised Constituency Parsing
We investigate the unsupervised constituency parsing task, which organizes words and phrases of a sentence into a hierarchical structure without using linguistically annotated data.
An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion.
Hierarchical Neural Data Synthesis for Semantic Parsing
Moreover, even the questions pertinent to a given domain, which are the input of a semantic parsing system, might not be readily available, especially in cross-domain semantic parsing.
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data
Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks.
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
A natural language database interface (NLDB) can democratize data-driven insights for non-technical users.
A Globally Normalized Neural Model for Semantic Parsing
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing.
Code Generation from Natural Language with Less Prior and More Monolingual Data
Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks.
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Optimizing Deeper Transformers on Small Datasets
This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.
Evaluating Lossy Compression Rates of Deep Generative Models
The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge.
Variational Hyper RNN for Sequence Modeling
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence.
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs
Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language.
On Variational Learning of Controllable Representations for Text without Supervision
The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space.
A Cross-Domain Transferable Neural Coherence Model
Coherence is an important aspect of text quality and is crucial for ensuring its readability.
Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer
In this work, we develop a novel regularizer to improve the learning of long-range dependency of sequence data.
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
We demonstrate the superiority of our method to the previous ones in two different continual learning settings on popular benchmarks, as well as a new continual learning problem where tasks are designed to be more dissimilar.
Adversarial Contrastive Estimation
Learning by contrasting positive and negative samples is a general strategy adopted by many methods.
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs).
Implicit Manifold Learning on Generative Adversarial Networks
This paper raises an implicit manifold learning perspective in Generative Adversarial Networks (GANs), by studying how the support of the learned distribution, modelled as a submanifold $\mathcal{M}_{\theta}$, perfectly match with $\mathcal{M}_{r}$, the support of the real data distribution.
Automatic Selection of t-SNE Perplexity
t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely used dimensionality reduction methods for data visualization, but it has a perplexity hyperparameter that requires manual selection.
Transductive Log Opinion Pool of Gaussian Process Experts
We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data.
Adversarial Manipulation of Deep Representations
We show that the representation of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations to the original image.
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models.
Efficient Optimization for Sparse Gaussian Process Regression
We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression.
