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Found 34 papers, 19 papers with code
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
Two main challenges of continual learning are catastrophic forgetting and task confusion.
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences.
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods.
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models.
Unsupervised Melody-to-Lyric Generation
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody.
Unsupervised Melody-Guided Lyrics Generation
At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
PLACES: Prompting Language Models for Social Conversation Synthesis
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns.
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings.
Variational Inference with Holder Bounds
In this work, we present a careful analysis of the thermodynamic variational objective (TVO), bridging the gap between existing variational objectives and shedding new insights to advance the field.
Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping
Distributed learning has become an integral tool for scaling up machine learning and addressing the growing need for data privacy.
Gradient Importance Learning for Incomplete Observations
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as classification.
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
Multi-Grained Knowledge Distillation for Named Entity Recognition
To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning. To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts.
Towards Robust and Efficient Contrastive Textual Representation Learning
There has been growing interest in representation learning for text data, based on theoretical arguments and empirical evidence.
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts.
Reconsidering Generative Objectives For Counterfactual Reasoning
As a step towards more flexible, scalable and accurate ITE estimation, we present a novel generative Bayesian estimation framework that integrates representation learning, adversarial matching and causal estimation.
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of primary interest.
Counterfactual Representation Learning with Balancing Weights
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type.
Double Robust Representation Learning for Counterfactual Prediction
Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences.
Improving Text Generation with Student-Forcing Optimal Transport
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
Variational Disentanglement for Rare Event Modeling
Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems.
Weakly supervised cross-domain alignment with optimal transport
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.
APo-VAE: Text Generation in Hyperbolic Space
In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations.
Variational Learning of Individual Survival Distributions
The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making.
On Fenchel Mini-Max Learning
Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling.
Improving Textual Network Learning with Variational Homophilic Embeddings
This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool for network analysis.
Survival Function Matching for Calibrated Time-to-Event Predictions
We present a survival function estimator for probabilistic predictions in time-to-event models, based on a neural network model for draws from the distribution of event times, without explicit assumptions on the form of the distribution.
Improving Sequence-to-Sequence Learning via Optimal Transport
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE).
Adversarial Text Generation via Feature-Mover's Distance
However, the discrete nature of text hinders the application of GAN to text-generation tasks.
Variational Inference and Model Selection with Generalized Evidence Bounds
Recent advances on the scalability and flexibility of variational inference have made it successful at unravelling hidden patterns in complex data.
Chi-square Generative Adversarial Network
To assess the difference between real and synthetic data, Generative Adversarial Networks (GANs) are trained using a distribution discrepancy measure.
Adversarial Time-to-Event Modeling
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice.
