nils-herrmann/clean_scirepeval huggingface dataset:
Effect of Eye Misalignment on Ocular Dominance according to BCM and PCA Synaptic Modification.

In this paper we realistically model a two-eye visual environment and study its effect on single cell synaptic modification. In particular, we study the effect of image misalignment on receptive field formation after eye opening. We show that binocular misalignment effects PCA and BCM learning in different ways. For the BCM learning rule this misalignment is sufficient to produce varying degrees of ocular dominance, whereas for PCA learning binocular neurons emerge in every case. Network results seem dominated by single cell results. Such differences should help us distinguish between these learning rules.
An integrated approach to the study of object features in visual recognition

We propose to assess the relevance of theories of synaptic modification as models of feature extraction in human vision, by using masks derived from synaptic weight patterns to occlude parts of the stimulus images in psychophysical experiments. In the experiment reported here, we found that a mask derived from principal component analysis. of object images was more effective in reducing the generalization performance of human subjects than a mask derived from another method of feature extraction (BCM). based on higher-order statistics of the images.
Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head

Imitation is a powerful mechanism for transferring knowledge from an instructor to a naïve observer, one that is deeply contingent on a state of shared attention between these two agents. In this paper we present Bayesian algorithms that implement the core of an imitation learning framework. We use gaze imitation, coupled with task-dependent saliency learning, to build a state of shared attention between the instructor and observer. We demonstrate the performance of our algorithms in a gaze following and saliency learning task implemented on an active vision robotic head. Our results suggest that the ability to follow gaze and learn instructor-and task-specific saliency models could play a crucial role in building systems capable of complex forms of human-robot interaction.
A Model-Based Goal-Directed Bayesian Framework for Imitation Learning in Humans and Machines

Imitation offers a powerful mechanism for knowledge acquisition, particularly for intelligent agents (like infants) that lack the ability to transfer knowledge using language. Several algorithms and models have recently been proposed for imitation learning in humans and robots. However, few proposals offer a framework for imitation learning in noisy stochastic environments where the imitator must learn and act under real-time performance constraints. In this paper, we present a novel probabilistic framework for imitation learning in stochastic environments with unreliable sensors. We develop Bayesian algorithms, based on Meltzoff and Moore’s AIM hypothesis for infant imitation, that implement the core of an imitation learning framework. Our algorithms are computationally efficient, allowing real-time learning and imitation in an active stereo vision robotic head. We present results of both software simulations and our algorithms running on the head, demonstrating the validity of our approach. We conclude by advocating a research agenda that
03-06-02 The Trajectory Mixture Model for Learning Collections of Nonlinear Functions

Learning statistical models of nonlinear dynamical systems has long been an important problem in machine learning. The problem becomes especially hard when the dynamical system is composed of a mixture of nonlinear models, not just a single nonlinear model. To address this general case of nonlinear time-series modeling, we propose a new hierarchical architecture: the Trajectory Mixture Model (TMM). The TMM learns collections of different nonlinear “trajectories” through state space. The model uses an expectation maximization (EM) algorithm to train a collection of nonlinear function approximators based on Gaussian radial basis function units. State densities are represented using samples estimated by particle filtering and smoothing. A sample-based representation provides an effective means of representing non-parametric state densities that change arbitrarily over time. We use entropy-based model selection to ensure that the individual function approximators, as well as the higherlevel mixture model, do not overfit the data. Our results suggest that TMMs can learn complex nonlinear state space models directly from observations and may offer greater flexibility in modeling time-series data than existing methods such as extended Kalman filters and particle filters.
Learning with Deictic Representation

Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be a viable alternative: they promise generalization while allowing the use of existing reinforcement-learning methods. Yet, there are few experiments on learning with deictic representations reported in the literature. In this paper we explore the effectiveness of two forms of deictic representation and a naive propositional representation in a simple blocks-world domain. We find, empirically, that the deictic representations actually worsen performance. We conclude with a discussion of possible causes of these results and strategies for more effective learning in domains with objects. 1 This report describes work done within the Artificial Intelligence Lab at Massachusetts Institute of Technology. The research was sponsored by the Office of Naval Research contract N00014-00-1-0298, by the Nippon Telegraph & Telephone Corporation as part of the NTT/MIT Collaboration Agreement, and by a National Science Foundation Graduate Research Fellowship.
Planning with Probabilistic Rules in a Relational World

Being able to represent and reason about the world as though it were composed of “objects” seems like a useful abstraction. The typical approach to representing a world composed of objects is to use a relational representation; however, other representations, such as deictic representations, have also been studied. I am interested not only in an agent that is able to represent objects, but in one that is also able to act in order to achieve some task. This requires the ability to learn a plan of action. While value-based approaches to learning plans have been studied in the past, both with relational and deictic representations, I believe the shortcomings uncovered by those studies can be overcome by the use of a world model. Knowledge about how the world works has the advantage of being re-usable across specific tasks. In general, however, it is difficult to obtain a completely specified model about the world. This work attempts to characterize an approach to planning in a relational domain when the world model is represented as a potentially incomplete and/or redundant set of uncertain rules.
Learning with Deictic Representations

Humans speak, and probably think, of the world as being made up of objects. Individual objects have features, and sets of objects stand in various relations to one another. If artificial agents are to successfully interact with our world, they will need to represent and reason about objects, their features, and relations that hold among them. We are interested in how agents embedded in inherently relational environments, such as physically embodied robots, can learn to act.
Class Prediction from Time Series Gene Expression Profiles Using Dynamical Systems Kernels

We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal characteristics of the data. More specifically, we model the evolution of the gene expression profiles as a Linear Time Invariant (LTI) dynamical system and estimate its model parameters. A kernel on dynamical systems is then used to classify these time series. We successfully test our approach on a published dataset to predict response to drug therapy in Multiple Sclerosis patients. For pharmacogenomics, our method offers a huge potential for advanced computational tools in disease diagnosis, and disease and drug therapy outcome prognosis.
Leaving the Span

We discuss a simple sparse linear problem that is hard to learn with any algorithm that uses a linear combination of the training instances as its weight vector. The hardness holds even if we allow the learner to embed the instances into any higher dimensional feature space (and use a kernel function to define the dot product between the embedded instances). These algorithms are inherently limited by the fact that after seeing k instances only a weight space of dimension k can be spanned. Our hardness result is surprising because the same problem can be efficiently learned using the exponentiated gradient (EG) algorithm: Now the component-wise logarithms of the weights are essentially a linear combination of the training instances and after seeing k instances. This algorithm enforces additional constraints on. the weights (all must be non-negative and sum to one) and in some cases these constraints alone force the rank of the weight space to grow as fast as 2 k .
Depth-3 Arithmetic Circuits for Sn2(X) and Extensions of the Graham-Pollack Theorem

We consider the problem of computing the second elementary symmetric polynomial Sn2(X)Δ = +1≤i<j≤n XiXj using depth-three arithmetic circuits of the form +i=1r Πj=1Si Lij(X), where each Lij is a linear form. We consider this problem over several fields and determine exactly the number of multiplication gates required. The lower bounds are proved for inhomogeneous circuits where the Lij's are allowed to have constants; the upper bounds are proved in the homogeneous model. For reals and rationals the number of multiplication gates required is exactly n - 1; in most other cases, it is ⌈n/2⌉.

This problem is related to the Graham-Pollack theorem in algebraic graph theory. In particular, our results answer the following question of Babai and Frankl: what is the minimum number of complete bipartite graphs required to cover each edge of a complete graph an odd number of times? We show that for infinitely many n, the answer is ⌈n/2⌉.
Multilinear Polynomials and a Conjecture of Frankl and Füredi

Frankl and Furedi conjectured that given a family F of subsets of [n] such that 1?|E?F|?kfor all distinctEandFin F, we must have |F|??ki=0(n?1i), (1981, P. Frankl and Z. Furedi,). The first proof of this result was given by G. V. Ramanan in (). In this note, we present a proof which is a modification of an approach to this problem by H. Snevily () and like Snevily's, is based on the technique of N. Alon, L. Babai, and H. Suzuki (.
Competitive Algorithms for Layered Graph Traversal

A layered graph is a connected graph whose vertices are partitioned into sets L0=s, L1, L2,..., and whose edges, which have nonnegative integral weights, run between consecutive layers. Its width is $\max\{|L_i|\}$. In the on-line layered graph traversal problem, a searcher starts at s in a layered graph of unknown width and tries to reach a target vertex t; however, the vertices in layer i and the edges between layers i-1 and i are only revealed when the searcher reaches layer i-1. 
We give upper and lower bounds on the competitive ratio of layered graph traversal algorithms. We give a deterministic on-line algorithm which is O(9w)-competitive on width-w graphs and prove that for no w can a deterministic on-line algorithm have a competitive ratio better than 2w-2 on width-w graphs. We prove that for all w, w/2 is a lower bound on the competitive ratio of any randomized on-line layered graph traversal algorithm. For traversing layered graphs consisting of w disjoint paths tied together at a common source, we give a randomized on-line algorithm with a competitive ratio of O(log w) and prove that this is optimal up to a constant factor.
Approximation algorithms for the achromatic number

The achromatic number for a graph G=?V,E? is the largest integer m such that there is a partition of V into disjoint independent sets {V1,?,Vm} such that for each pair of distinct sets Vi, Vj, Vi?Vj is not an independent set in G. Yannakakis and Gavril (1980,  Math.38, 364?372) proved that determining this value for general graphs is NP-complete. For n-vertex graphs we present the first o(n) approximation algorithm for this problem. We also present an O(n5/12) approximation algorithm for graphs with girth at least 5 and a constant approximation algorithm for trees.
An O(log*n) approximation algorithm for the asymmetric p-center problem

The input to the asymmetricp-center problem consists of an integerpand ann×ndistance matrixDdefined on a vertex setVof sizen, wheredijgives the distance fromitoj. The distances are assumed to obey the triangle inequality. For a subsetS?Vthe radius ofSis the minimum distanceRsuch that every point inVis at a distance at mostRfrom some point inS. Thep-center problem consists of picking a setS?Vof sizepto minimize the radius. This problem is known to be NP-complete.For the symmetric case, whendij=dji, approximation algorithms that deliver a solution to within 2 of the optimal are known. David Shmoys, in his article 11, mentions that nothing was known about the asymmetric case. We present an algorithm that achieves a ratio ofO(log*n).
Rapidly Mixing Markov Chains

The computational di culty of the problems of counting the elements of a nite set of combinatorial structures has led to the development of random ized algorithms The problem of generating at random an element of a set with some property is central for various randomized algorithms because of its connection with counting problem A technique based on Markov chain is used to generate uniformly at random the element of set This leads to polynomial time randomized algorithm if the Markov chain is rapidly mixing
An -center problem

A brassiere including a pair of bust supporting cups having an outer panel means and an inner segment, an underbust band assembly, a pair of back band portions, and a pair of shoulder straps connected to the cups and to the back band portions. The generally crescent-shaped inner segments extend from the top of each cup to the lower central area of the cup such that when strain is exerted from the force of the breasts, the inner segments are activated and function to reshape the breast and avoid distorting and misshaping the outer cup panel means.
Constructing Reliable Communication Networks of Small Weight Online

Suppose the vertices of a complete weighted graph are revealed to us one at a time, and we have to build a network with some desired properties on these vertices. We consider the cost of building a network which has these properties after the addition of each vertex and compare it to the cost incurred by an algorithm which builds a network that has the same properties only at the end, i.e., after all the vertices have been revealed. We examine online algorithms for constructing networks with three properties; high connectivity, low vertex degree, and small diameter, and evaluate these algorithms from the competitive perspective. We show that on n points drawn from any metric space, a greedy algorithm constructs a k -connected network with a competitive ratio of O ( k 2 log n ), and prove a lower bound of Ω( k log n ) on the competitive ratio of any online algorithm. We also present an online algorithm with a competitive ratio of O ( k 2 log n ) to build a k -connected network with maximum vertex degree 3 k and an online algorithm with a competitive ratio of O ( k 2 log n ) to build a k -connected network with a diameter no more than a constant times the diameter of the n points.
Lazy Approximation : A New Approach for Solving Continuous Finite-Horizon MDPs

Solving Markov decision processes (MDPs) with continuous state spaces is a challenge due to, among other problems, the well-known curse of dimensionality. Nevertheless, numerous real-world applications such as transportation planning and telescope observation scheduling exhibit a critical dependence on continuous states. Current approaches to continuous-state MDPs include discretizing their transition models. In this paper, we propose and study an alternative, discretization-free approach we call lazy approximation. Empirical study shows that lazy approximation performs much better than discretization, and we successfully applied this new technique to a more realistic planetary rover planning problem.
Lazy Approximation for Solving Continuous Finite-Horizon MDPs

Solving Markov decision processes (MDPs) with continuous state spaces is a challenge due to, among other problems. the well-known curse of dimensionality. Nevertheless, numerous real-world applications such as transportation planning and telescope observation scheduling exhibit a critical dependence on continuous states. Current approaches to continuous-state MDPs include discretizing their transition models. In this paper, we propose and study an alternative, discretization-free approach we call lazy approximation. Empirical study shows that lazy approximation performs much better than discretization, and we successfully applied this new technique to a more realistic planetary rover planning problem.
A theoretical analysis of Model-Based Interval Estimation

Several algorithms for learning near-optimal policies in Markov Decision Processes have been analyzed and proven efficient. Empirical results have suggested that Model-based Interval Estimation (MBIE) learns efficiently in practice, effectively balancing exploration and exploitation. This paper presents the first theoretical analysis of MBIE, proving its efficiency even under worst-case conditions. The paper also introduces a new performance metric, average loss, and relates it to its less "online" cousins from the literature.
Cyclic Equilibria in Markov Games

Although variants of value iteration have been proposed for finding Nash or correlated equilibria in general-sum Markov games, these variants have not been shown to be effective in general. In this paper, we demonstrate by construction that existing variants of value iteration cannot find stationary equilibrium policies in arbitrary general-sum Markov games. Instead, we propose an alternative interpretation of the output of value iteration based on a new (non-stationary) equilibrium concept that we call "cyclic equilibria." We prove that value iteration identifies cyclic equilibria in a class of games in which it fails to find stationary equilibria. We also demonstrate empirically that value iteration finds cyclic equilibria in nearly all examples drawn from a random distribution of Markov games.
Corpus-based Learning of Analogies and Semantic Relations

We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning “A is to B as C is to D”; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct; the average college-bound senior high school student answers about 57% correctly). We motivate this research by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as “laser printer”, according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for both verbal analogies and noun-modifier relations.
PPDDL 1 . 0 : An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects

We desribe a variation of the planning domain definition language, PDDL, that permits the modeling of probabilistic planning problems with rewards. This language, PPDDL1.0, was used as the input language for the probabilistic track of the 4th International Planning Competition. We provide the complete syntax for PPDDL1.0 and give a semantics of PPDDL1.0 planning problems in terms of Markov decision processes. Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA
An empirical evaluation of interval estimation for Markov decision processes

This work takes an empirical approach to evaluating three model-based reinforcement-learning methods. All methods intend to speed the learning process by mixing exploitation of learned knowledge with exploration of possibly promising alternatives. We consider /spl epsi/-greedy exploration, which is computationally cheap and popular, but unfocused in its exploration effort; R-Max exploration, a simplification of an exploration scheme that comes with a theoretical guarantee of efficiency; and a well-grounded approach, model-based interval estimation, that better integrates exploration and exploitation. Our experiments indicate that effective exploration can result in dramatic improvements in the observed rate of learning.
Exploration via Model-based Interval Estimation

This paper takes an empirical approach to evaluating three model-based reinforcementlearning methods. All methods intend to speed the learning process by mixing exploitation of learned knowledge with exploration of possibly promising alternatives. We consider -greedy exploration, which is computationally cheap and popular, but unfocused in its exploration effort; R-Max exploration, a simplification of an exploration scheme that comes with a theoretical guarantee of efficiency; and a well-grounded approach, model-based interval estimation, that better integrates exploration and exploitation and achieves the best performance in our example tasks. Our experiments indicate that effective exploration can result in dramatic improvements in the observed rate of learning.
Stochastic Boolean Satisfiability

Satisfiability problems and probabilistic models are core topics of artificial intelligence and computer science. This paper looks at the rich intersection between these two areas, opening the door for the use of satisfiability approaches in probabilistic domains. The paper examines a generic stochastic satisfiability problem, SSAT, which can function for probabilistic domains as SAT does for deterministic domains. It shows the connection between SSAT and well-studied problems in belief network inference and planning under uncertainty, and defines algorithms, both systematic and stochastic, for solving SSAT instances. These algorithms are validated on random SSAT formulae generated under the fixed-clause model. In spite of the large complexity gap between SSAT (PSPACE) and SAT (NP), the paper suggests that much of what we have learned about SAT transfers to the probabilistic domain.