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Found 16 papers, 4 papers with code
Dynamic Scheduled Sampling with Imitation Loss for Neural Text Generation
Scheduled sampling is a curriculum learning strategy that gradually exposes the model to its own predictions during training to mitigate this bias.
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization
We also introduce a number of state-of-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images.
Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling
Given the claims of improved text generation quality across various pre-trained neural models, we consider the coherence evaluation of machine generated text to be one of the principal applications of coherence models that needs to be investigated.
Perturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation
The proposed scheme is evaluated by numerical simulations of a single carrier optical fiber communication system operating at 32 Gbaud with 64-quadrature amplitude modulation and 20*80 km transmission distance.
Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation
Advanced large-scale neural language models have led to significant success in many language generation tasks.
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
To the best of our knowledge, this is the first targeted label attack technique.
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
This dynamic defense can prohibit the adversary from selecting an optimal substitute model for black-box attacks.
A Token Level Multi-target Stance Detection Dataset
It leads to lacking fine-grained analysis and generalization ability of the stance detection algorithm.
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks
Although coherence modeling has come a long way in developing novel models, their evaluation on downstream applications for which they are purportedly developed has largely been neglected.
Resurrecting Submodularity for Neural Text Generation
The resulting attention module offers an architecturally simple and empirically effective method to improve the coverage of neural text generation.
Fiber Nonlinearity Mitigation via the Parzen Window Classifier for Dispersion Managed and Unmanaged Links
Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects.
Hierarchical Pointer Net Parsing
Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity.
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing
We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST).
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
In particular, we use a bipartite network to construct the user-item network, and represent the interactions among users (or items) by the corresponding one-mode projection network.
Can Adversarial Network Attack be Defended?
In this paper, we are interested in the possibility of defense against adversarial attack on network, and propose defense strategies for GNNs against attacks.
Social and Information Networks Physics and Society
A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation
In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal-noise interactions, which are not taken into account by digital back propagation.
