Search Results for author:
Found 19 papers, 9 papers with code
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI
In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution.
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work.
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay.
Zephyr: Direct Distillation of LM Alignment
Starting from a dataset of outputs ranked by a teacher model, we apply distilled direct preference optimization (dDPO) to learn a chat model with significantly improved intent alignment.
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
Deep Learning based Forecasting: a case study from the online fashion industry
These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption.
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
Temporal data such as time series can be viewed as discretized measurements of the underlying function.
Intrinsic Anomaly Detection for Multi-Variate Time Series
We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection.
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting
Time series models aim for accurate predictions of the future given the past, where the forecasts are used for important downstream tasks like business decision making.
Context-invariant, multi-variate time series representations
Modern time series corpora, in particular those coming from sensor-based data, exhibit characteristics that have so far not been adequately addressed in the literature on representation learning for time series.
Probabilistic Time Series Forecasting with Implicit Quantile Networks
Here, we propose a general method for probabilistic time series forecasting.
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
+1
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.
Set Flow: A Permutation Invariant Normalizing Flow
We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data.
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP
We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models.
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward.
Stochastic Maximum Likelihood Optimization via Hypernetworks
This work explores maximum likelihood optimization of neural networks through hypernetworks.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category.
