Above are papers related to the following paper: [Paper_ID] Paper #1122574. 	[Title] Tau Net: A Neural Network for Modeling Temporal Variability [Abstract] The ability to handle temporal variation is important when dealing with real-world dynamic signals. In many applications, inputs do not come in as fixed-rate sequences, but rather as signals with time scales that can vary from one instance to the next; thus, modeling dynamic signals requires not only the ability to recognize sequences but also the ability to handle temporal changes in the signal. This paper discusses "Tau Net," a neural network for modeling dynamic signals, and its application to speech. In Tau Net, sequence learning is accomplished using a combination of prediction, recurrence and time-delay connections. Temporal variability is modeled by having adaptable time constants in the network, which are adjusted with respect to the prediction error. Adapting the time constants changes the time scale of the network, and the adapted value of the network's time constant provides a measure of temporal variation in the signal. Tau Net has been applied to several simple signals: sets of sine waves differing in frequency and in phase [2], a multidimensional signal representing the walking gait of children [3], and the energy contour of a simple speech utterance [11]. Tau Net has also been shown to work on a voicing distinction task using synthetic speech data [12]. In this paper, Tau Net is applied to two speaker-independent tasks, vowel recognition (of f/ae/,/iy/,/ux/g) and consonant recognition (of f/p/,/t/,/k/g) using speech data taken from the TIMIT database. It is shown that Tau Nets, trained on medium-rate tokens, achieved about the same performance as networks without time constants trained on tokens at all rates, and performed better than networks without time constants trained on medium-rate tokens. Our results demonstrate Tau Net's ability to identify vowels and consonants at variable speech rates by extrapolating to rates not represented in the training set. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112426. 	[Title] REDUCED MEMORY REPRESENTATIONS FOR MUSIC [Abstract] We address the problem of musical variation (identification of different musical sequences as variations) and its implications for mental representations of music. According to reductionist theories, listeners judge the structural importance of musical events while forming mental representations. These judgments may result from the production of reduced memory representations that retain only the musical gist. In a study of improvised music performance, pianists produced variations on melodies. Analyses of the musical events retained across variations provided support for the reductionist account of structural importance. A neural network trained to produce reduced memory representations for the same melodies represented structurally important events more efficiently than others. Agreement among the musicians' improvisations, the network model, and music-theoretic predictions suggest that perceived constancy across musical variation is a natural result of a reductionist mechanism for producing memory representations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #29708. 	[Title] Diffusion of Context and Credit Information in Markovian Models [Abstract] This paper studies the problem of ergodicity of transition probability matrices in Marko-vian models, such as hidden Markov models (HMMs), and how it makes very difficult the task of learning to represent long-term context for sequential data. This phenomenon hurts the forward propagation of long-term context information, as well as learning a hidden state representation to represent long-term context, which depends on propagating credit information backwards in time. Using results from Markov chain theory, we show that this problem of diffusion of context and credit is reduced when the transition probabilities approach 0 or 1, i.e., the transition probability matrices are sparse and the model essentially deterministic. The results found in this paper apply to learning approaches based on continuous optimization, such as gradient descent and the Baum-Welch algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #131117. 	[Title] Learning Sequential Tasks by Incrementally Adding Higher Orders [Abstract] An incremental, higher-order, non-recurrent network combines two properties found to be useful for learning sequential tasks: higher-order connections and incremental introduction of new units. The network adds higher orders when needed by adding new units that dynamically modify connection weights. Since the new units modify the weights at the next time-step with information from the previous step, temporal tasks can be learned without the use of feedback, thereby greatly simplifying training. Furthermore, a theoretically unlimited number of units can be added to reach into the arbitrarily distant past. Experiments with the Reber grammar have demonstrated speedups of two orders of magnitude over recurrent networks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #29492. 	[Title] Induction of Multiscale Temporal Structure [Abstract] Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to find structure in sequences, in practice they are not sufficiently powerful to discover arbitrary contingencies, especially those spanning long temporal intervals or involving high order statistics. For example, in designing a connectionist network for music composition, we have encountered the problem that the net is able to learn musical structure that occurs locally in time|e.g., relations among notes within a musical phrase|but not structure that occurs over longer time periods|e.g., relations among phrases. To address this problem, we require a means of constructing a reduced description of the sequence that makes global aspects more explicit or more readily detectable. I propose to achieve this using hidden units that operate with different time constants. Simulation experiments indicate that slower time-scale hidden units are able to pick up global structure, structure that simply can not be learned by standard Many patterns in the world are intrinsically temporal, e.g., speech, music, the unfolding of events. Recurrent neural net architectures have been devised to accommodate time-varying sequences. For example, the architecture shown in Figure 1 can map a sequence of inputs to a sequence of outputs. Learning structure in temporally-extended sequences is a difficult computational problem because the input pattern may not contain all the task-relevant information at any instant. Thus, back propagation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144408. 	[Title] Neural net architectures for temporal sequence processing [Abstract] I present a general taxonomy of neural net architectures for processing time-varying patterns. This taxonomy subsumes many existing architectures in the literature, and points to several promising architectures that have yet to be examined. Any architecture that processes time-varying patterns requires two conceptually distinct components: a short-term memory that holds on to relevant past events and an associator that uses the short-term memory to classify or predict. My taxonomy is based on a characterization of short-term memory models along the dimensions of form, content, and adaptability. Experiments on predicting future values of a financial time series (US dollar-Swiss franc exchange rates) are presented using several alternative memory models. The results of these experiments serve as a baseline against which more sophisticated architectures can be compared. Neural networks have proven to be a promising alternative to traditional techniques for nonlinear temporal prediction tasks (e.g., Curtiss, Brandemuehl, & Kreider, 1992; Lapedes & Farber, 1987; Weigend, Huberman, & Rumelhart, 1992). However, temporal prediction is a particularly challenging problem because conventional neural net architectures and algorithms are not well suited for patterns that vary over time. The prototypical use of neural nets is in structural pattern recognition. In such a task, a collection of features|visual, semantic, or otherwise|is presented to a network and the network must categorize the input feature pattern as belonging to one or more classes. For example, a network might be trained to classify animal species based on a set of attributes describing living creatures such as "has tail", "lives in water", or "is carnivorous"; or a network could be trained to recognize visual patterns over a two-dimensional pixel array as a letter in fA; B; . . . ; Zg. In such tasks, the network is presented with all relevant information simultaneously. In contrast, temporal pattern recognition involves processing of patterns that evolve over time. The appropriate response at a particular point in time depends not only on the current input, but potentially all previous inputs. This is illustrated in Figure 1, which shows the basic framework for a temporal prediction problem. I assume that time is quantized into discrete steps, a sensible assumption because many time series of interest are intrinsically discrete, and continuous series can be sampled at a fixed interval. The input at time t is denoted x(t). For univariate series, this input Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #400473. 	[Title] Sequence Learning with Incremental Higher-Order Neural Networks [Abstract] An incremental, higher-order, non-recurrent neural-network combines two properties found to be useful for sequence learning in neural-networks: higher-order connections and the incremental introduction of new units. The incremental, higher-order neural-network adds higher orders when needed by adding new units that dynamically modify connection weights. The new units modify the weights at the next time-step with information from the previous step. Since a theoretically unlimited number of units can be added to the network, information from the arbitrarily distant past can be brought to bear on each prediction. Temporal tasks can thereby be learned without the use of feedback, in contrast to recurrent neural-networks. Because there are no recurrent connections, training is simple and fast. Experiments have demonstrated speedups of two orders of magnitude over recurrent networks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #152219. 	[Title] A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages [Abstract] We present a neural net architecture that can discover hierarchical and recursive structure in symbol strings. To detect structure at multiple levels, the architecture has the capability of reducing symbols substrings to single symbols, and makes use of an external stack memory. In terms of formal languages, the architecture can learn to parse strings in an LR(0) context-free grammar. Given training sets of positive and negative exemplars, the architecture has been trained to recognize many different grammars. The architecture has only one layer of modifiable weights, allowing for a Many cognitive domains involve complex sequences that contain hierarchical or recursive structure, e.g., music, natural language parsing, event perception. To illustrate, "the spider that ate the hairy fly" is a noun phrase containing the embedded noun phrase "the hairy fly." Understanding such multilevel structures requires forming reduced descriptions (Hinton, 1988) in which a string of symbols or states ("the hairy fly") is reduced to a single symbolic entity (a noun phrase). We present a neural net architecture that learns to encode the structure of symbol strings via such reduction transformations. The difficult problem of extracting multilevel structure from complex, extended sequences has been studied by Mozer (1992), Ring (1993), Rohwer (1990), and Schmidhuber (1992), among others. While these previous efforts have made some straightforward interpretation of its behavior. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3229. 	[Title] Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2 [Abstract] Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models Please summarise all the given information.

Paper #1122574 has the title `Tau Net: A Neural Network for Modeling Temporal Variability  
` and the abstract `The ability to handle temporal variation is important when dealing with real-world dynamic signals. In many applications, inputs do not come in as fixed-rate sequences, but rather as signals with time scales that can vary from one instance to the next; thus, modeling dynamic signals requires not only the ability to recognize sequences but also the ability to handle temporal changes in the signal. This paper discusses "Tau Net," a neural network for modeling dynamic signals, and its application to speech. In Tau Net, sequence learning is accomplished using a combination of prediction, recurrence and time-delay connections. Temporal variability is modeled by having adaptable time constants in the network, which are adjusted with respect to the prediction error. Adapting the time constants changes the time scale of the network, and the adapted value of the network's time constant provides a measure of temporal variation in the signal. Tau Net has been applied to several simple signals: sets of sine waves differing in frequency and in phase [2], a multidimensional signal representing the walking gait of children [3], and the energy contour of a simple speech utterance [11]. Tau Net has also been shown to work on a voicing distinction task using synthetic speech data [12]. In this paper, Tau Net is applied to two speaker-independent tasks, vowel recognition (of f/ae/,/iy/,/ux/g) and consonant recognition (of f/p/,/t/,/k/g) using speech data taken from the TIMIT database. It is shown that Tau Nets, trained on medium-rate tokens, achieved about the same performance as networks without time constants trained on tokens at all rates, and performed better than networks without time constants trained on medium-rate tokens. Our results demonstrate Tau Net's ability to identify vowels and consonants at variable speech rates by extrapolating to rates not represented in the training set. 
`.
Question: Which category should Paper #1122574 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1122574 has the title `Tau Net: A Neural Network for Modeling Temporal Variability  
` and the abstract `The ability to handle temporal variation is important when dealing with real-world dynamic signals. In many applications, inputs do not come in as fixed-rate sequences, but rather as signals with time scales that can vary from one instance to the next; thus, modeling dynamic signals requires not only the ability to recognize sequences but also the ability to handle temporal changes in the signal. This paper discusses "Tau Net," a neural network for modeling dynamic signals, and its application to speech. In Tau Net, sequence learning is accomplished using a combination of prediction, recurrence and time-delay connections. Temporal variability is modeled by having adaptable time constants in the network, which are adjusted with respect to the prediction error. Adapting the time constants changes the time scale of the network, and the adapted value of the network's time constant provides a measure of temporal variation in the signal. Tau Net has been applied to several simple signals: sets of sine waves differing in frequency and in phase [2], a multidimensional signal representing the walking gait of children [3], and the energy contour of a simple speech utterance [11]. Tau Net has also been shown to work on a voicing distinction task using synthetic speech data [12]. In this paper, Tau Net is applied to two speaker-independent tasks, vowel recognition (of f/ae/,/iy/,/ux/g) and consonant recognition (of f/p/,/t/,/k/g) using speech data taken from the TIMIT database. It is shown that Tau Nets, trained on medium-rate tokens, achieved about the same performance as networks without time constants trained on tokens at all rates, and performed better than networks without time constants trained on medium-rate tokens. Our results demonstrate Tau Net's ability to identify vowels and consonants at variable speech rates by extrapolating to rates not represented in the training set. 
`.
Question: Which category should Paper #1122574 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks