In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
Vector Symbolic Architectures (VSAs) are high-dimensional vector representations of objects (eg., words, image parts), relations (eg., sentence structures), and sequences for use with machine learning algorithms. They consist of a vector addition operator for representing a collection of unordered objects, a Binding operator for associating groups of objects, and a methodology for encoding complex structures. We first develop Constraints that machine learning imposes upon VSAs: for example, similar structures must be represented by similar vectors. The constraints suggest that current VSAs should represent phrases (“The smart Brazilian girl”) by binding sums of terms, in addition to simply binding the terms directly. We show that matrix multiplication can be used as the binding operator for a VSA, and that matrix elements can be chosen at random. A consequence for living systems is that binding is mathematically possible without the need to specify, in advance, precise neuron-to-neuron connection properties for large numbers of synapses. A VSA that incorporates these ideas, MBAT (Matrix Binding of Additive Terms), is described that satisfies all Constraints. With respect to machine learning, for some types of problems appropriate VSA representations permit us to prove learnability, rather than relying on simulations. We also propose dividing machine (and neural) learning and representation into three Stages, with differing roles for learning in each stage. For neural modeling, we give “representational reasons” for nervous systems to have many recurrent connections, as well as for the importance of phrases in language processing. Sizing simulations and analyses suggest that VSAs in general, and MBAT in particular, are ready for real-world applications.

Ex Output:
Representing Objects, Relations, and Sequences


Ex Input:
In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains 43.9/47.7% WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with 15.7% and 5.3% relative improvement respectively.

Ex Output:
HIGHWAY LONG SHORT-TERM MEMORY RNNS FOR DISTANT SPEECH RECOGNITION


Ex Input:
Several authors have explained that the like­ lihood ratio measures the strength of the evi­ dence represented by observations in statisti­ cal problems. This idea works fine when the goal is to evaluate the strength of the avail­ able evidence for a simple hypothesis versus another simple hypothesis. However, the ap­ plicability of this idea is limited to simple hypotheses because the likelihood function is primarily defined on points simple hy­ potheses of the parameter space. In this paper we define a general weight of evidence that is applicable to both simple and compos­ ite hypotheses. It is based on the Dempster­ Shafer concept of plausibility and is shown to be a generalization of the likelihood ratio. Functional models are of a fundamental im­ portance for the general weight of evidence proposed in this paper. The relevant con­ cepts and ideas are explained by means of a familiar urn problem and the general analysis of a real-world medical problem is presented.

Ex Output:

From Likelihood to Plausibility