2 research outputs found
Unbounded cache model for online language modeling with open vocabulary
Recently, continuous cache models were proposed as extensions to recurrent
neural network language models, to adapt their predictions to local changes in
the data distribution. These models only capture the local context, of up to a
few thousands tokens. In this paper, we propose an extension of continuous
cache models, which can scale to larger contexts. In particular, we use a large
scale non-parametric memory component that stores all the hidden activations
seen in the past. We leverage recent advances in approximate nearest neighbor
search and quantization algorithms to store millions of representations while
searching them efficiently. We conduct extensive experiments showing that our
approach significantly improves the perplexity of pre-trained language models
on new distributions, and can scale efficiently to much larger contexts than
previously proposed local cache models.Comment: Accepted to NIPS 201
Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series
There exist many approaches for description and recognition of unseen classes
in datasets. Nevertheless, it becomes a challenging problem when we deal with
multivariate time-series (MTS) (e.g., motion data), where we cannot apply the
vectorial algorithms directly to the inputs. In this work, we propose a novel
multiple-kernel dictionary learning (MKD) which learns semantic attributes
based on specific combinations of MTS dimensions in the feature space. Hence,
MKD can fully/partially reconstructs the unseen classes based on the training
data (seen classes). Furthermore, we obtain sparse encodings for unseen classes
based on the learned MKD attributes, and upon which we propose a simple but
effective incremental clustering algorithm to categorize the unseen MTS classes
in an unsupervised way. According to the empirical evaluation of our MKD
framework on real benchmarks, it provides an interpretable reconstruction of
unseen MTS data as well as a high performance regarding their online
clustering.Comment: 6 pages, ESANN 2019 conferenc