22 research outputs found
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis
Anomaly detection in database management systems (DBMSs) is difficult because
of increasing number of statistics (stat) and event metrics in big data system.
In this paper, I propose an automatic DBMS diagnosis system that detects
anomaly periods with abnormal DB stat metrics and finds causal events in the
periods. Reconstruction error from deep autoencoder and statistical process
control approach are applied to detect time period with anomalies. Related
events are found using time series similarity measures between events and
abnormal stat metrics. After training deep autoencoder with DBMS metric data,
efficacy of anomaly detection is investigated from other DBMSs containing
anomalies. Experiment results show effectiveness of proposed model, especially,
batch temporal normalization layer. Proposed model is used for publishing
automatic DBMS diagnosis reports in order to determine DBMS configuration and
SQL tuning.Comment: 8 page
Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers
Autoregressive transformers have shown remarkable success in video
generation. However, the transformers are prohibited from directly learning the
long-term dependency in videos due to the quadratic complexity of
self-attention, and inherently suffering from slow inference time and error
propagation due to the autoregressive process. In this paper, we propose
Memory-efficient Bidirectional Transformer (MeBT) for end-to-end learning of
long-term dependency in videos and fast inference. Based on recent advances in
bidirectional transformers, our method learns to decode the entire
spatio-temporal volume of a video in parallel from partially observed patches.
The proposed transformer achieves a linear time complexity in both encoding and
decoding, by projecting observable context tokens into a fixed number of latent
tokens and conditioning them to decode the masked tokens through the
cross-attention. Empowered by linear complexity and bidirectional modeling, our
method demonstrates significant improvement over the autoregressive
Transformers for generating moderately long videos in both quality and speed.
Videos and code are available at https://sites.google.com/view/mebt-cvpr2023
Variational Distribution Learning for Unsupervised Text-to-Image Generation
We propose a text-to-image generation algorithm based on deep neural networks
when text captions for images are unavailable during training. In this work,
instead of simply generating pseudo-ground-truth sentences of training images
using existing image captioning methods, we employ a pretrained CLIP model,
which is capable of properly aligning embeddings of images and corresponding
texts in a joint space and, consequently, works well on zero-shot recognition
tasks. We optimize a text-to-image generation model by maximizing the data
log-likelihood conditioned on pairs of image-text CLIP embeddings. To better
align data in the two domains, we employ a principled way based on a
variational inference, which efficiently estimates an approximate posterior of
the hidden text embedding given an image and its CLIP feature. Experimental
results validate that the proposed framework outperforms existing approaches by
large margins under unsupervised and semi-supervised text-to-image generation
settings.Comment: Accepted at CVPR202
Forecasting Taxi Demands with Fully Convolutional Networks and Temporal Guided Embedding
Learning complex spatiotemporal patterns is a key to predict future taxi demand volumes. We propose temporal guided networks (TGNet), which is an efficient model architecture with fully convolutional networks and temporal guided em- bedding, to capture spatiotemporal patterns. Existing approaches use complex architectures, historical demands (day/week/month ago) to capture the recurring patterns, and external data sources such as meteorological, traffic flow, or tex- ture data. However, TGNet only uses fully convolutional networks and temporal guided embedding without those external data sources. In this study, only pick-up and drop-off volumes of NYC-taxi dataset are used to utilize the full potential of the hidden patterns in the historical data points. We show that TGNet provides notable performance gains on a real-world benchmark, NYC-taxi dataset, over previous state-of-the-art models. Finally we explain how to extend our architecture to incorporate external data sources.1