Above are papers related to the following paper: [Paper_ID] Paper #411005. 	[Title] GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS [Abstract] Numerous recent papers focus on standard recurrent nets' problems with long time lags between relevant signals. Some propose rather sophisticated, alternative methods. We show: many problems used to test previous methods can be solved more quickly by random weight guessing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8581. 	[Title] LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992) [Abstract] I propose a novel general principle for unsupervised learning of distributed non-redundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter `abstract concepts' out of the environmental input such that these concepts are statistically independent of those upon which the other units focus. I discuss various simple yet potentially powerful implementations of the principle which aim at finding binary factorial codes (Bar-low et al., 1989), i.e. codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also non-linear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #24966. 	[Title] A Practical Bayesian Framework for Backprop Networks [Abstract] A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability and the Bayesian evidence is obtained. This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a). 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 #13208. 	[Title] ON LEARNING HOW TO LEARN LEARNING STRATEGIES [Abstract] This paper introduces the "incremental self-improvement paradigm". Unlike previous methods, incremental self-improvement encourages a reinforcement learning system to improve the way it learns, and to improve the way it improves the way it learns ..., without significant theoretical limitations | the system is able to "shift its inductive bias" in a universal way. Its major features are: (1) There is no explicit difference between "learning", "meta-learning", and other kinds of information processing. Using a Turing machine equivalent programming language, the system itself occasionally executes self-delimiting, initially highly random "self-modification programs" which modify the context-dependent probabilities of future action sequences (including future self-modification programs). (2) The system keeps only those probability modifications computed by "useful" self-modification programs: those which bring about more payoff (reward, reinforcement) per time than all previous self-modification programs. (3) The computation of payoff per time takes into account all the computation time required for learning | the entire system life is considered: boundaries between learning trials are ignored (if there are any). A particular implementation based on the novel paradigm is presented. It is designed to exploit what conventional digital machines are good at: fast storage addressing, arithmetic operations etc. Experiments illustrate the system's mode of operation. Keywords: Self-improvement, self-reference, introspection, machine-learning, reinforcement learning. Note: This is the revised and extended version of an earlier report from November 24, 1994. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #29723. 	[Title] Boltzmann Chains and Hidden Markov Models [Abstract] We propose a statistical mechanical framework for the modeling of discrete time series. Maximum likelihood estimation is done via Boltzmann learning in one-dimensional networks with tied weights. We call these networks Boltzmann chains and show that they contain hidden Markov models (HMMs) as a special case. Our framework also motivates new architectures that address particular shortcomings of HMMs. We look at two such architectures: parallel chains that model feature sets with disparate time scales, and looped networks that model long-term dependencies between hidden states. For these networks, we show how to implement the Boltzmann learning rule exactly, in polynomial time, without resort to simulated or mean-field annealing. The necessary computations are done by exact decimation procedures from statistical mechanics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13212. 	[Title] DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997 [Abstract] Many neural net learning algorithms aim at finding "simple" nets to explain training data. The expectation is: the "simpler" the networks, the better the generalization on test data (! Occam's razor). Previous implementations, however, use measures for "simplicity" that lack the power, universality and elegance of those based on Kolmogorov complexity and Solomonoff's algorithmic probability. Likewise, most previous approaches (especially those of the "Bayesian" kind) suffer from the problem of choosing appropriate priors. This paper addresses both issues. It first reviews some basic concepts of algorithmic complexity theory relevant to machine learning, and how the Solomonoff-Levin distribution (or universal prior) deals with the prior problem. The universal prior leads to a probabilistic method for finding "algorithmically simple" problem solutions with high generalization capability. The method is based on Levin complexity (a time-bounded generalization of Kolmogorov complexity) and inspired by Levin's optimal universal search algorithm. For a given problem, solution candidates are computed by efficient "self-sizing" programs that influence their own runtime and storage size. The probabilistic search algorithm finds the "good" programs (the ones quickly computing algorithmically probable solutions fitting the training data). Simulations focus on the task of discovering "algorithmically simple" neural networks with low Kolmogorov complexity and high generalization capability. It is demonstrated that the method, at least with certain toy problems where it is computationally feasible, can lead to generalization results unmatchable by previous neural net algorithms. Much remains do be done, however, to make large scale applications and "incremental learning" feasible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13213. 	[Title] A GENERAL METHOD FOR INCREMENTAL SELF-IMPROVEMENT AND MULTI-AGENT LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #231198. 	[Title] A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING [Abstract] The RTRL algorithm for fully recurrent continually running networks (Robinson and Fallside, 1987)(Williams and Zipser, 1989) requires O(n 4 ) computations per time step, where n is the number of non-input units. I describe a method suited for on-line learning which computes exactly the same gradient and requires fixed-size storage of the same order but has an average time complexity 1 per time step of O(n 3 ). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #267824. 	[Title] LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS (Neural Computation, 4(1):131-139, 1992) [Abstract] Previous algorithms for supervised sequence learning are based on dynamic recurrent networks. This paper describes an alternative class of gradient-based systems consisting of two feedforward nets that learn to deal with temporal sequences using fast weights: The first net learns to produce context dependent weight changes for the second net whose weights may vary very quickly. The method offers the potential for STM storage efficiency: A single weight (instead of a full-fledged unit) may be sufficient for storing temporal information. Various learning methods are derived. Two experiments with unknown time delays illustrate the approach. One experiment shows how the system can be used for adaptive temporary variable binding. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #214472. 	[Title] A Computer Scientist's View of Life, the Universe, and Everything [Abstract] Is the universe computable? If so, it may be much cheaper in terms of information requirements to compute all computable universes instead of just ours. I apply basic concepts of Kolmogorov complexity theory to the set of possible universes, and chat about perceived and true randomness, life, generalization, and learning in a given universe. Assumptions. A long time ago, the Great Programmer wrote a program that runs all possible universes on His Big Computer. "Possible" means "computable": (1) Each universe evolves on a discrete time scale. (2) Any universe's state at a given time is describable by a finite number of bits. One of the many universes is ours, despite some who evolved in it and claim it is incomputable. Computable universes. Let T M denote an arbitrary universal Turing machine with unidirectional output tape. T M 's input and output symbols are "0", "1", and "," (comma). T M 's possible input programs can be ordered alphabetically: "" (empty program), "0", "1", ",", "00", "01", "0,", "10", "11", "1,", ",0", ",1", ",,", "000", etc. Let A k denote T M 's k-th program in this list. Its output will be a finite or infinite string over the alphabet f "0","1",","g. This sequence of bitstrings separated by commas will be interpreted as the evolution E k of universe U k . If E k includes at least one comma, then let U l k represents U k 's state at the l-th time step of E k (k; l 2 f1; 2; : : : ; g). E k is represented by the sequence U 1 k corresponds to U k 's big bang. Different algorithms may compute the same universe. Some universes are finite (those whose programs cease producing outputs at some point), others are not. I don't know about ours. TM not important. The choice of the Turing machine is not important. This is due to the compiler theorem: for each universal Turing machine C there exists a constant prefix C 2 f "0","1",","g fl such that for all possible programs p, C's output in response to program C p is identical to T M 's output in response to p. The prefix C is the compiler that compiles programs for T M into equivalent programs for C. k denote the l-th (possibly empty) bitstring before the l-th comma. U l Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69198. 	[Title] LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992) [Abstract] Previous neural network learning algorithms for sequence processing are computationally expensive and perform poorly when it comes to long time lags. This paper first introduces a simple principle for reducing the descriptions of event sequences without loss of information. A consequence of this principle is that only unexpected inputs can be relevant. This insight leads to the construction of neural architectures that learn to `divide and conquer' by recursively decomposing sequences. I describe two architectures. The first functions as a self-organizing multi-level hierarchy of recurrent networks. The second, involving only two recurrent networks, tries to collapse a multi-level predictor hierarchy into a single recurrent net. Experiments show that the system can require less computation per time step and many fewer training sequences than conventional training algorithms for recurrent nets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28504. 	[Title] TD Models: Modeling the World at a Mixture of Time Scales [Abstract] Temporal-difference (TD) learning can be used not just to predict rewards, as is commonly done in reinforcement learning, but also to predict states, i.e., to learn a model of the world's dynamics. We present theory and algorithms for intermixing TD models of the world at different levels of temporal abstraction within a single structure. Such multi-scale TD models can be used in model-based reinforcement-learning architectures and dynamic programming methods in place of conventional Markov models. This enables planning at higher and varied levels of abstraction, and, as such, may prove useful in formulating methods for hierarchical or multi-level planning and reinforcement learning. In this paper we treat only the prediction problem|that of learning a model and value function for the case of fixed agent behavior. Within this context, we establish the theoretical foundations of multi-scale models and derive TD algorithms for learning them. Two small computational experiments are presented to test and illustrate the theory. This work is an extension and generalization of the work of Singh (1992), Dayan (1993), and Sutton & Pinette (1985). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #170338. 	[Title] Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study [Abstract] We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive "probabilistic prototype trees" to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #358884. 	[Title] THE DISCOVERY OF ALGORITHMIC PROBABILITY [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19697. 	[Title] Keeping Neural Networks Simple by Minimizing the Description Length of the Weights [Abstract] Supervised neural networks generalize well if there is much less information in the weights than there is in the output vectors of the training cases. So during learning, it is important to keep the weights simple by penalizing the amount of information they contain. The amount of information in a weight can be controlled by adding Gaussian noise and the noise level can be adapted during learning to optimize the trade-off between the expected squared error of the network and the amount of information in the weights. We describe a method of computing the derivatives of the expected squared error and of the amount of information in the noisy weights in a network that contains a layer of non-linear hidden units. Provided the output units are linear, the exact derivatives can be computed efficiently without time-consuming Monte Carlo simulations. The idea of minimizing the amount of information that is required to communicate the weights of a neural network leads to a number of interesting schemes for encoding the weights. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #131315. 	[Title] Hierarchical Recurrent Neural Networks for Long-Term Dependencies [Abstract] We have already shown that extracting long-term dependencies from sequential data is difficult, both for deterministic dynamical systems such as recurrent networks, and probabilistic models such as hidden Markov models (HMMs) or input/output hidden Markov models (IOHMMs). In practice, to avoid this problem, researchers have used domain specific a-priori knowledge to give meaning to the hidden or state variables representing past context. In this paper, we propose to use a more general type of a-priori knowledge, namely that the temporal dependencies are structured hierarchically. This implies that long-term dependencies are represented by variables with a long time scale. This principle is applied to a recurrent network which includes delays and multiple time scales. Experiments confirm the advantages of such structures. A similar approach is proposed for HMMs and IOHMMs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #131317. 	[Title] LONG SHORT-TERM MEMORY Neural Computation 9(8):1735-1780, 1997 [Abstract] Learning to store information over extended time intervals via recurrent backpropagation takes a very long time, mostly due to insufficient, decaying error back flow. We briefly review Hochreiter's 1991 analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called "Long Short-Term Memory" (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete time steps by enforcing constant error flow through "constant error carrousels" within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with RTRL, BPTT, Recurrent Cascade-Correlation, Elman nets, and Neural Sequence Chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long time lag tasks that have never been solved by previous recurrent network algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #131318. 	[Title] FLAT MINIMA Neural Computation 9(1):1-42 (1997) [Abstract] We present a new algorithm for finding low complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight-space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to "simple" networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require Gaussian assumptions and does not depend on a "good" weight prior instead we have a prior over input/output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second order derivatives, it has backprop's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms (1) conventional backprop, (2) weight decay, (3) "optimal brain surgeon" / "optimal brain damage". We also provide pseudo code of the algorithm (omitted from the NC-version). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105148. 	[Title] ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time. [Abstract] A reinforcement learning system with limited computational resources interacts with an unrestricted, unknown environment. Its goal is to maximize cumulative reward, to be obtained throughout its limited, unknown lifetime. System policy is an arbitrary modifiable algorithm mapping environmental inputs and internal states to outputs and new internal states. The problem is: in realistic, unknown environments, each policy modification process (PMP) occurring during system life may have unpredictable influence on environmental states, rewards and PMPs at any later time. Existing reinforcement learning algorithms cannot properly deal with this. Neither can naive exhaustive search among all policy candidates | not even in case of very small search spaces. In fact, a reasonable way of measuring performance improvements in such general (but typical) situations is missing. I define such a measure based on the novel "reinforcement acceleration criterion" (RAC). At a given time, RAC is satisfied if the beginning of each completed PMP that computed a currently valid policy modification has been followed by long-term acceleration of average reinforcement intake (the computation time for later PMPs is taken into account). I present a method called "environment-independent reinforcement acceleration" (EIRA) which is guaranteed to achieve RAC. EIRA does neither care whether the system's policy allows for changing itself, nor whether there are multiple, interacting learning systems. Consequences are: (1) a sound theoretical framework for "meta-learning" (because the success of a PMP recursively depends on the success of all later PMPs, for which it is setting the stage). (2) A sound theoretical framework for multi-agent learning. The principles have been implemented (1) in a single system using an assembler-like programming language to modify its own policy, and (2) a system consisting of multiple agents, where each agent is in fact just a connection in a fully recurrent reinforcement learning neural net. A by-product of this research is a general reinforcement learning algorithm for such nets. Preliminary experiments illustrate the theory. Please summarise all the given information.

Paper #411005 has the title `GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS  
` and the abstract `Numerous recent papers focus on standard recurrent nets' problems with long time lags between relevant signals. Some propose rather sophisticated, alternative methods. We show: many problems used to test previous methods can be solved more quickly by random weight guessing. 
`.
Question: Which category should Paper #411005 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #411005 has the title `GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS  
` and the abstract `Numerous recent papers focus on standard recurrent nets' problems with long time lags between relevant signals. Some propose rather sophisticated, alternative methods. We show: many problems used to test previous methods can be solved more quickly by random weight guessing. 
`.
Question: Which category should Paper #411005 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