Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders for sequences, which allows to maximize the short-term memory and that can be solved with a closed-form solution without backpropagation… We introduce an initialization schema that pretrains the weights of a recurrent neural network to approximate the linear autoencoder of the input sequences and we show how such pretraining can better support solving hard classification tasks […]

We consider a natural model of online preference aggregation, where sets of preferred items $R_1, R_2, ldots, R_t$ along with a demand for $k_t$ items in each $R_t$, appear online. Without prior knowledge of $(R_t, k_t)$, the learner maintains a ranking $pi_t$ aiming that at least $k_t$ items from $R_t$ appear high in $pi_t$… This is a fundamental problem in preference aggregation with applications to, e.g., ordering product or news items in web pages based on user scrolling and click […]

We propose a novel medical term embedding method named CODER, which stands for mediCal knOwledge embeDded tErm Representation. CODER is designed for medical term normalization by providing close vector representations for terms that represent the same or similar concepts with multi-language support… CODER is trained on top of BERT (Devlin et al., 2018) with the innovation that token vector aggregation is trained using relations from the UMLS Metathesaurus (Bodenreider, 2004), which is a comprehensive medical knowledge graph with multi-language support. […]

Contrastive loss and its variants have become very popular recently for learning visual representations without supervision. In this work, we first generalize the standard contrastive loss based on cross entropy to a broader family of losses that share an abstract form of $mathcal{L}_{text{alignment}} + lambda mathcal{L}_{text{distribution}}$, where hidden representations are encouraged to (1) be aligned under some transformations/augmentations, and (2) match a prior distribution of high entropy… We show that various instantiations of the generalized loss perform similarly under the […]

Platooning is a critical technology to realize autonomous driving. Each vehicle in platoons adopts the IEEE 802.11p standard to exchange information through communications to maintain the string stability of platoons… However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i.e., aerodynamics drag and rolling resistance moment etc. Disturbances acting on one vehicle may inevitably affect the following vehicles and cause that the spacing error […]