188 research outputs found
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.Comment: ACM Multimedia 201
Continual Learning with Dirichlet Generative-based Rehearsal
Recent advancements in data-driven task-oriented dialogue systems (ToDs)
struggle with incremental learning due to computational constraints and
time-consuming issues. Continual Learning (CL) attempts to solve this by
avoiding intensive pre-training, but it faces the problem of catastrophic
forgetting (CF). While generative-based rehearsal CL methods have made
significant strides, generating pseudo samples that accurately reflect the
underlying task-specific distribution is still a challenge. In this paper, we
present Dirichlet Continual Learning (DCL), a novel generative-based rehearsal
strategy for CL. Unlike the traditionally used Gaussian latent variable in the
Conditional Variational Autoencoder (CVAE), DCL leverages the flexibility and
versatility of the Dirichlet distribution to model the latent prior variable.
This enables it to efficiently capture sentence-level features of previous
tasks and effectively guide the generation of pseudo samples. In addition, we
introduce Jensen-Shannon Knowledge Distillation (JSKD), a robust logit-based
knowledge distillation method that enhances knowledge transfer during pseudo
sample generation. Our experiments confirm the efficacy of our approach in both
intent detection and slot-filling tasks, outperforming state-of-the-art
methods
Efficient deep data assimilation with sparse observations and time-varying sensors
Variational Data Assimilation (DA) has been broadly used in engineering
problems for field reconstruction and prediction by performing a weighted
combination of multiple sources of noisy data. In recent years, the integration
of deep learning (DL) techniques in DA has shown promise in improving the
efficiency and accuracy in high-dimensional dynamical systems. Nevertheless,
existing deep DA approaches face difficulties in dealing with unstructured
observation data, especially when the placement and number of sensors are
dynamic over time. We introduce a novel variational DA scheme, named
Voronoi-tessellation Inverse operator for VariatIonal Data assimilation
(VIVID), that incorporates a DL inverse operator into the assimilation
objective function. By leveraging the capabilities of the Voronoi-tessellation
and convolutional neural networks, VIVID is adept at handling sparse,
unstructured, and time-varying sensor data. Furthermore, the incorporation of
the DL inverse operator establishes a direct link between observation and state
space, leading to a reduction in the number of minimization steps required for
DA. Additionally, VIVID can be seamlessly integrated with Proper Orthogonal
Decomposition (POD) to develop an end-to-end reduced-order DA scheme, which can
further expedite field reconstruction. Numerical experiments in a fluid
dynamics system demonstrate that VIVID can significantly outperform existing DA
and DL algorithms. The robustness of VIVID is also accessed through the
application of various levels of prior error, the utilization of varying
numbers of sensors, and the misspecification of error covariance in DA
Toward Learning Model-Agnostic Explanations for Deep Learning-Based Signal Modulation Classifiers
Recent advances in deep learning (DL) have brought tremendous gains in signal modulation classification. However, DL-based classifiers lack transparency and interpretability, which raises concern about model's reliability and hinders the wide deployment in real-word applications. While explainable methods have recently emerged, little has been done to explain the DL-based signal modulation classifiers. In this work, we propose a novel model-agnostic explainer, Model-Agnostic Signal modulation classification Explainer (MASE), which provides explanations for the predictions of black-box modulation classifiers. With the subsequence-based signal interpretable representation and in-distribution local signal sampling, MASE learns a local linear surrogate model to derive a class activation vector, which assigns importance values to the timesteps of signal instance. Besides, the constellation-based explanation visualization is adopted to spotlight the important signal features relevant to model prediction. We furthermore propose the first generic quantitative explanation evaluation framework for signal modulation classification to automatically measure the faithfulness, sensitivity, robustness, and efficiency of explanations. Extensive experiments are conducted on two real-world datasets with four black-box signal modulation classifiers. The quantitative results indicate MASE outperforms two state-of-the-art methods with 44.7% improvement in faithfulness, 30.6% improvement in robustness, and 44.1% decrease in sensitivity. Through qualitative visualizations, we further demonstrate the explanations of MASE are more human interpretable and provide better understanding into the reliability of black-box model decisions
A Mutual Information-based Assessment of Reverse Engineering on Rewards of Reinforcement Learning
MMBench: Is Your Multi-modal Model an All-around Player?
Large vision-language models have recently achieved remarkable progress,
exhibiting great perception and reasoning abilities concerning visual
information. However, how to effectively evaluate these large vision-language
models remains a major obstacle, hindering future model development.
Traditional benchmarks like VQAv2 or COCO Caption provide quantitative
performance measurements but suffer from a lack of fine-grained ability
assessment and non-robust evaluation metrics. Recent subjective benchmarks,
such as OwlEval, offer comprehensive evaluations of a model's abilities by
incorporating human labor, but they are not scalable and display significant
bias. In response to these challenges, we propose MMBench, a novel
multi-modality benchmark. MMBench methodically develops a comprehensive
evaluation pipeline, primarily comprised of two elements. The first element is
a meticulously curated dataset that surpasses existing similar benchmarks in
terms of the number and variety of evaluation questions and abilities. The
second element introduces a novel CircularEval strategy and incorporates the
use of ChatGPT. This implementation is designed to convert free-form
predictions into pre-defined choices, thereby facilitating a more robust
evaluation of the model's predictions. MMBench is a systematically-designed
objective benchmark for robustly evaluating the various abilities of
vision-language models. We hope MMBench will assist the research community in
better evaluating their models and encourage future advancements in this
domain. Project page: https://opencompass.org.cn/mmbench
MiR-20a regulates the PRKG1 gene by targeting its coding region in pulmonary arterial smooth muscle cells
AbstractChronic hypoxia triggers pulmonary vascular remodeling, which is associated with de-differentiation of pulmonary artery smooth muscle cells (PASMC). Here, we show that miR-20a expression is up-regulated in response to hypoxia in both mouse and human PASMC. We also observed that miR-20a represses the protein kinase, cGMP-dependent, type I (PRKG1) gene and we identified two crucial miR-20a binding sites within the coding region of PRKG1. Functional studies showed that miR-20a promotes the proliferation and migration of human PASMC, whereas it inhibits their differentiation. In summary, we provided a possible mechanism by which hypoxia results in decreased PRKG1 expression and in the phenotypic switching of PASMC
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