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Found 7 papers, 2 papers with code
Evaluating Automatic Metrics with Incremental Machine Translation Systems
We introduce a dataset comprising commercial machine translations, gathered weekly over six years across 12 translation directions.
ICU: Conquering Language Barriers in Vision-and-Language Modeling by Dividing the Tasks into Image Captioning and Language Understanding
Most multilingual vision-and-language (V&L) research aims to accomplish multilingual and multimodal capabilities within one model.
Representations of Domains via CF-approximation Spaces
It is proved that the family of CF-closed sets in a CF-approximation space endowed with set-inclusion order is a continuous domain and that every continuous domain is isomorphic to the family of CF-closed sets of some CF-approximation space endowed with set-inclusion order.
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization
In this paper we consider the training stability of recurrent neural networks (RNNs) and propose a family of RNNs, namely SBO-RNN, that can be formulated using stochastic bilevel optimization (SBO).
Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution
Lightweight neural networks refer to deep networks with small numbers of parameters, which are allowed to be implemented in resource-limited hardware such as embedded systems.
Rating Facts under Coarse-to-fine Regimes
After training, class similarity is sensible over the multi-class datasets, especially in the fine-grained one.
Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle
In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle.
