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Found 25 papers, 9 papers with code
Weakly Supervised Extractive Summarization with Attention
Automatic summarization aims to extract important information from large amounts of textual data in order to create a shorter version of the original texts while preserving its information.
Lumos : Empowering Multimodal LLMs with Scene Text Recognition
We introduce Lumos, the first end-to-end multimodal question-answering system with text understanding capabilities.
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimation
In particular, we present an efficient two-stage gradient boosting framework for short-term traffic state estimation.
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
For that, we use more than 100, 000 research papers and build up a knowledge network with more than 64, 000 concept nodes.
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
Learning to Transfer for Traffic Forecasting via Multi-task Learning
In particular, we present a multi-task learning framework for temporal and spatio-temporal domain adaptation of traffic forecasting models.
Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting
In this work, we present a method for training retrieval-based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset.
Context-Aware Scene Graph Generation With Seq2Seq Transformers
In this task, the model needs to detect objects and predict visual relationships between them.
Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings
We then improve an XLM-based unsupervised neural MT system pre-trained on Wikipedia by supplementing it with pseudo-parallel text mined from the same corpus, boosting unsupervised translation performance by up to 3. 5 BLEU on the WMT'14 French-English and WMT'16 German-English tasks and outperforming the previous state-of-the-art.
The Multilingual Amazon Reviews Corpus
We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification.
Efficient and Information-Preserving Future Frame Prediction and Beyond
Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios.
Ranked #14 on
Video Prediction
on Moving MNIST
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings
Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language.
Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances
We discuss the problem of echographic transcription in autoregressive sequence-to-sequence attentional architectures for automatic speech recognition, where a model produces very long sequences of repetitive outputs when presented with out-of-domain utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning Effective Visual Relationship Detector on 1 GPU
We present our winning solution to the Open Images 2019 Visual Relationship challenge.
CrevNet: Conditionally Reversible Video Prediction
Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios.
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER
We report the magnitude of the improvement on the multilingual MLDoc text classification and CoNLL 2002/2003 named entity recognition tasks.
Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting
To test our models, a customer service agent handles live contacts and at each turn we present the top four model responses and allow the agent to select (and optionally edit) one of the suggestions or to type their own.
A neural interlingua for multilingual machine translation
We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture.
A practical approach to dialogue response generation in closed domains
We describe a prototype dialogue response generation model for the customer service domain at Amazon.
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis
In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computationally and storage expensive.
Large scale canonical correlation analysis with iterative least squares
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems.
Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent
We propose a new two stage algorithm LING for large scale regression problems.
New Subsampling Algorithms for Fast Least Squares Regression
We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data ($n \gg p$).
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform
We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations ($p \gg n$).
