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Found 9 papers, 1 papers with code
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022
Our cascade system consists of 1) Conformer RNN-T automatic speech recognition model, 2) punctuation-capitalization model based on pre-trained T5 encoder, 3) ensemble of Transformer neural machine translation models fine-tuned on TED talks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies
This paper describes Amazon Alexa AI’s implementation for the IWSLT 2022 shared task on formality control.
Learning with Holographic Reduced Representations
HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space.
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment
We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective.
Locality Preserving Loss: Neighbors that Live together, Align together
We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations.
Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter
Another way to remove that limitation, an optional classification layer, trained on manually annotated DoS attack tweets, to filter out non-attack tweets can be used to increase precision at the expense of recall.
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning
In this paper, we proposeaprobabilistic abductive reasoningapproach that augments an exist-ing rule-based IDS (snort [29]) to detect these evolved attacks by (a)Predicting rule conditions that are likely to occur (based on existingrules) and (b) able to generate new snort rules when provided withseed rule (i. e. a starting rule) to reduce the burden on experts toconstantly update them.
Fashioning with Networks: Neural Style Transfer to Design Clothes
Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis.
Identifying Spatial Relations in Images using Convolutional Neural Networks
Traditional approaches to building a large scale knowledge graph have usually relied on extracting information (entities, their properties, and relations between them) from unstructured text (e. g. Dbpedia).
