35 research outputs found
Child acquisition of referring expressions
Children, like adults, use referring expressions to refer to specific objects, events, or people. Research has provided insights into how children use referring expressions and the appearance of forms developmentally (Radford, 1990; Abu-Akel, et al., 2004; Pine & Lieven, 1997). This study examined how three, four, and five year-old children use referring expressions across increasingly more decontextualized tasks as defined by the Situational-Discourse-Semantic (SDS) Model (Norris & Hoffman, 1993, 2002) . The participants included 4 three-year-old, 12 four-year-old, and 20 five-year-old children. Language samples were elicited using seven tasks of increasing difficulty. The referring expressions produced for each task were categorized based on their usage, and then analyzed for similarities and differences in the frequencies and types of referring expressions used within and between contextualized and decontextualized levels, tasks, and age groups. A significant difference was found in how participants across the three different ages used referring expressions in the contextualized tasks versus the decontextualized tasks. The relationship between the task and category also revealed that the task significantly affected the number of referring expressions found in a given category across all of the participant age groups. Lastly, the difference between the participants in the three different age groups and the tasks was examined. Tasks 3, 6, and 7 all showed a significant group difference for performance on these tasks. Through this study, we have gained insights into referring expressions, including what they are and how they are used in contextualized and decontextualized language samples. In examining the language samples, we have started to explore how children use referring expressions, including the use of cohesive ties and types of errors children produce. Although looking at the language samples from this syntactic perspective is useful, this study also considers the effects of context and meaning and how these semantic-pragmatic variables affect the use of referring expressions. In addition, this study provides some early insights into effects of changing context and how this interacts with age
Explorations in Parallel Linear Genetic Programming
Linear Genetic Programming (LGP) is a powerful problem-solving technique,
but one with several significant weaknesses. LGP programs consist
of a linear sequence of instructions, where each instruction may reuse
previously computed results. This structure makes LGP programs compact
and powerful, however it also introduces the problem of instruction
dependencies. The notion of instruction dependencies expresses the concept
that certain instructions rely on other instructions. Instruction dependencies
are often disrupted during crossover or mutation when one or
more instructions undergo modification. This disruption can cause disproportionately
large changes in program output resulting in non-viable
offspring and poor algorithm performance.
Motivated by biological inspiration and the issue of code disruption,
we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs
consist of n lists of instructions. These lists are executed in parallel, and
the resulting vectors are summed to produce the overall program output.
PLGP limits the disruptive effects of crossover and mutation, which allows
PLGP to significantly outperform regular LGP.
We examine the PLGP architecture and determine that large PLGP programs
can be slow to converge. To improve the convergence time of large
PLGP programs we develop a new form of PLGP called Cooperative Coevolution
PLGP (CC PLGP). CC PLGP adapts the concept of cooperative
coevolution to the PLGP architecture. CC PLGP optimizes all program
components in parallel, allowing CC PLGP to converge significantly faster
than conventional PLGP.
We examine the CC PLGP architecture and determine that performanc
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons
Rotation equivariance is a desirable property in many practical applications
such as motion forecasting and 3D perception, where it can offer benefits like
sample efficiency, better generalization, and robustness to input
perturbations. Vector Neurons (VN) is a recently developed framework offering a
simple yet effective approach for deriving rotation-equivariant analogs of
standard machine learning operations by extending one-dimensional scalar
neurons to three-dimensional "vector neurons." We introduce a novel
"VN-Transformer" architecture to address several shortcomings of the current VN
models. Our contributions are: we derive a rotation-equivariant attention
mechanism which eliminates the need for the heavy feature preprocessing
required by the original Vector Neurons models; we extend the VN
framework to support non-spatial attributes, expanding the applicability of
these models to real-world datasets; we derive a rotation-equivariant
mechanism for multi-scale reduction of point-cloud resolution, greatly speeding
up inference and training; we show that small tradeoffs in equivariance
(-approximate equivariance) can be used to obtain large improvements
in numerical stability and training robustness on accelerated hardware, and we
bound the propagation of equivariance violations in our models. Finally, we
apply our VN-Transformer to 3D shape classification and motion forecasting with
compelling results.Comment: Published in Transactions on Machine Learning Research (TMLR), 2023;
Previous version appeared in Workshop on Machine Learning for Autonomous
Driving, Conference on Neural Information Processing Systems (NeurIPS), 202
Online Meta-learning by Parallel Algorithm Competition
The efficiency of reinforcement learning algorithms depends critically on a
few meta-parameters that modulates the learning updates and the trade-off
between exploration and exploitation. The adaptation of the meta-parameters is
an open question in reinforcement learning, which arguably has become more of
an issue recently with the success of deep reinforcement learning in
high-dimensional state spaces. The long learning times in domains such as Atari
2600 video games makes it not feasible to perform comprehensive searches of
appropriate meta-parameter values. We propose the Online Meta-learning by
Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several
instances of a reinforcement learning algorithm are run in parallel with small
differences in the initial values of the meta-parameters. After a fixed number
of episodes, the instances are selected based on their performance in the task
at hand. Before continuing the learning, Gaussian noise is added to the
meta-parameters with a predefined probability. We validate the OMPAC method by
improving the state-of-the-art results in stochastic SZ-Tetris and in standard
Tetris with a smaller, 1010, board, by 31% and 84%, respectively, and
by improving the results for deep Sarsa() agents in three Atari 2600
games by 62% or more. The experiments also show the ability of the OMPAC method
to adapt the meta-parameters according to the learning progress in different
tasks.Comment: 15 pages, 10 figures. arXiv admin note: text overlap with
arXiv:1702.0311
SPAM detection: Naïve bayesian classification and RPN expression-based LGP approaches compared
An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters. © Springer International Publishing Switzerland 2016