4,301 research outputs found
On Reverse Engineering in the Cognitive and Brain Sciences
Various research initiatives try to utilize the operational principles of
organisms and brains to develop alternative, biologically inspired computing
paradigms and artificial cognitive systems. This paper reviews key features of
the standard method applied to complexity in the cognitive and brain sciences,
i.e. decompositional analysis or reverse engineering. The indisputable
complexity of brain and mind raise the issue of whether they can be understood
by applying the standard method. Actually, recent findings in the experimental
and theoretical fields, question central assumptions and hypotheses made for
reverse engineering. Using the modeling relation as analyzed by Robert Rosen,
the scientific analysis method itself is made a subject of discussion. It is
concluded that the fundamental assumption of cognitive science, i.e. complex
cognitive systems can be analyzed, understood and duplicated by reverse
engineering, must be abandoned. Implications for investigations of organisms
and behavior as well as for engineering artificial cognitive systems are
discussed.Comment: 19 pages, 5 figure
Neural Paraphrase Identification of Questions with Noisy Pretraining
We present a solution to the problem of paraphrase identification of
questions. We focus on a recent dataset of question pairs annotated with binary
paraphrase labels and show that a variant of the decomposable attention model
(Parikh et al., 2016) results in accurate performance on this task, while being
far simpler than many competing neural architectures. Furthermore, when the
model is pretrained on a noisy dataset of automatically collected question
paraphrases, it obtains the best reported performance on the dataset
Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?
Correlations in the signal observed via functional Magnetic Resonance Imaging
(fMRI), are expected to reveal the interactions in the underlying neural
populations through hemodynamic response. In particular, they highlight
distributed set of mutually correlated regions that correspond to brain
networks related to different cognitive functions. Yet graph-theoretical
studies of neural connections give a different picture: that of a highly
integrated system with small-world properties: local clustering but with short
pathways across the complete structure. We examine the conditional independence
properties of the fMRI signal, i.e. its Markov structure, to find realistic
assumptions on the connectivity structure that are required to explain the
observed functional connectivity. In particular we seek a decomposition of the
Markov structure into segregated functional networks using decomposable graphs:
a set of strongly-connected and partially overlapping cliques. We introduce a
new method to efficiently extract such cliques on a large, strongly-connected
graph. We compare methods learning different graph structures from functional
connectivity by testing the goodness of fit of the model they learn on new
data. We find that summarizing the structure as strongly-connected networks can
give a good description only for very large and overlapping networks. These
results highlight that Markov models are good tools to identify the structure
of brain connectivity from fMRI signals, but for this purpose they must reflect
the small-world properties of the underlying neural systems
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences
We create a new NLI test set that shows the deficiency of state-of-the-art
models in inferences that require lexical and world knowledge. The new examples
are simpler than the SNLI test set, containing sentences that differ by at most
one word from sentences in the training set. Yet, the performance on the new
test set is substantially worse across systems trained on SNLI, demonstrating
that these systems are limited in their generalization ability, failing to
capture many simple inferences.Comment: 6 pages, short paper at ACL 201
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