9,287 research outputs found
Bayesian Grammar Induction for Language Modeling
We describe a corpus-based induction algorithm for probabilistic context-free
grammars. The algorithm employs a greedy heuristic search within a Bayesian
framework, and a post-pass using the Inside-Outside algorithm. We compare the
performance of our algorithm to n-gram models and the Inside-Outside algorithm
in three language modeling tasks. In two of the tasks, the training data is
generated by a probabilistic context-free grammar and in both tasks our
algorithm outperforms the other techniques. The third task involves
naturally-occurring data, and in this task our algorithm does not perform as
well as n-gram models but vastly outperforms the Inside-Outside algorithm.Comment: 8 pages, LaTeX, uses aclap.st
Regional Economic Impacts of the 1996 U.S. Peanut Program
Agricultural and Food Policy,
ECONOMIC IMPLICATIONS OF THE FAIR ACT ON U.S. PEANUT PRODUCERS
This study analyzed the potential economic impacts of the FAIR Act under GATT and NAFTA on the U.S. peanut industry. Results indicate that the economic impacts of the new program combined with the trade agreements are profound on the peanut industry in both short and long terms. Changes of the peanut program could decrease peanut producers' farm income substantially, eliminate government financial costs related to excessive quotas, and transfer peanut growers' program benefits back to peanut consumers. Increasing imports of foreign peanuts due to free/reduced trade barrier agreements would transfer peanut producers' program benefits to domestic peanut importers and foreign exporters who sell peanuts to the U.S. Note: Tables 3 and 4 not included in machine readable file--contact authors for copies.economic impacts, FAIR Act, peanuts, quota, support price, Agricultural and Food Policy, Crop Production/Industries,
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
While neuroevolution (evolving neural networks) has a successful track record
across a variety of domains from reinforcement learning to artificial life, it
is rarely applied to large, deep neural networks. A central reason is that
while random mutation generally works in low dimensions, a random perturbation
of thousands or millions of weights is likely to break existing functionality,
providing no learning signal even if some individual weight changes were
beneficial. This paper proposes a solution by introducing a family of safe
mutation (SM) operators that aim within the mutation operator itself to find a
degree of change that does not alter network behavior too much, but still
facilitates exploration. Importantly, these SM operators do not require any
additional interactions with the environment. The most effective SM variant
capitalizes on the intriguing opportunity to scale the degree of mutation of
each individual weight according to the sensitivity of the network's outputs to
that weight, which requires computing the gradient of outputs with respect to
the weights (instead of the gradient of error, as in conventional deep
learning). This safe mutation through gradients (SM-G) operator dramatically
increases the ability of a simple genetic algorithm-based neuroevolution method
to find solutions in high-dimensional domains that require deep and/or
recurrent neural networks (which tend to be particularly brittle to mutation),
including domains that require processing raw pixels. By improving our ability
to evolve deep neural networks, this new safer approach to mutation expands the
scope of domains amenable to neuroevolution
ES Is More Than Just a Traditional Finite-Difference Approximator
An evolution strategy (ES) variant based on a simplification of a natural
evolution strategy recently attracted attention because it performs
surprisingly well in challenging deep reinforcement learning domains. It
searches for neural network parameters by generating perturbations to the
current set of parameters, checking their performance, and moving in the
aggregate direction of higher reward. Because it resembles a traditional
finite-difference approximation of the reward gradient, it can naturally be
confused with one. However, this ES optimizes for a different gradient than
just reward: It optimizes for the average reward of the entire population,
thereby seeking parameters that are robust to perturbation. This difference can
channel ES into distinct areas of the search space relative to gradient
descent, and also consequently to networks with distinct properties. This
unique robustness-seeking property, and its consequences for optimization, are
demonstrated in several domains. They include humanoid locomotion, where
networks from policy gradient-based reinforcement learning are significantly
less robust to parameter perturbation than ES-based policies solving the same
task. While the implications of such robustness and robustness-seeking remain
open to further study, this work's main contribution is to highlight such
differences and their potential importance
A Generalization of the Ramanujan Polynomials and Plane Trees
Generalizing a sequence of Lambert, Cayley and Ramanujan, Chapoton has
recently introduced a polynomial sequence Q_n:=Q_n(x,y,z,t) defined by Q_1=1,
Q_{n+1}=[x+nz+(y+t)(n+y\partial_y)]Q_n. In this paper we prove Chapoton's
conjecture on the duality formula: Q_n(x,y,z,t)=Q_n(x+nz+nt,y,-t,-z), and
answer his question about the combinatorial interpretation of Q_n. Actually we
give combinatorial interpretations of these polynomials in terms of plane
trees, half-mobile trees, and forests of plane trees. Our approach also leads
to a general formula that unifies several known results for enumerating trees
and plane trees.Comment: 20 pages, 2 tables, 8 figures, see also
http://math.univ-lyon1.fr/~gu
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