4,150 research outputs found
Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search
Retrieval pipelines commonly rely on a term-based search to obtain candidate
records, which are subsequently re-ranked. Some candidates are missed by this
approach, e.g., due to a vocabulary mismatch. We address this issue by
replacing the term-based search with a generic k-NN retrieval algorithm, where
a similarity function can take into account subtle term associations. While an
exact brute-force k-NN search using this similarity function is slow, we
demonstrate that an approximate algorithm can be nearly two orders of magnitude
faster at the expense of only a small loss in accuracy. A retrieval pipeline
using an approximate k-NN search can be more effective and efficient than the
term-based pipeline. This opens up new possibilities for designing effective
retrieval pipelines. Our software (including data-generating code) and
derivative data based on the Stack Overflow collection is available online
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
This paper proposes K-NRM, a kernel based neural model for document ranking.
Given a query and a set of documents, K-NRM uses a translation matrix that
models word-level similarities via word embeddings, a new kernel-pooling
technique that uses kernels to extract multi-level soft match features, and a
learning-to-rank layer that combines those features into the final ranking
score. The whole model is trained end-to-end. The ranking layer learns desired
feature patterns from the pairwise ranking loss. The kernels transfer the
feature patterns into soft-match targets at each similarity level and enforce
them on the translation matrix. The word embeddings are tuned accordingly so
that they can produce the desired soft matches. Experiments on a commercial
search engine's query log demonstrate the improvements of K-NRM over prior
feature-based and neural-based states-of-the-art, and explain the source of
K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric
tailored for matching query words to document words, and provides effective
multi-level soft matches
Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the
availability of large-scale image sets. However, fine-grained classification
remains a major challenge due to the annotation cost of large numbers of
fine-grained categories. This project shows that compelling classification
performance can be achieved on such categories even without labeled training
data. Given image and class embeddings, we learn a compatibility function such
that matching embeddings are assigned a higher score than mismatching ones;
zero-shot classification of an image proceeds by finding the label yielding the
highest joint compatibility score. We use state-of-the-art image features and
focus on different supervised attributes and unsupervised output embeddings
either derived from hierarchies or learned from unlabeled text corpora. We
establish a substantially improved state-of-the-art on the Animals with
Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate
that purely unsupervised output embeddings (learned from Wikipedia and improved
with fine-grained text) achieve compelling results, even outperforming the
previous supervised state-of-the-art. By combining different output embeddings,
we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for
Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and
Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed
and Daniel Walter and Honglak Lee and Bernt Schiele}
HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty
The way we perceive a sound depends on many aspects-- its ecological
frequency, acoustic features, typicality, and most notably, its identified
source. In this paper, we present the HCU400: a dataset of 402 sounds ranging
from easily identifiable everyday sounds to intentionally obscured artificial
ones. It aims to lower the barrier for the study of aural phenomenology as the
largest available audio dataset to include an analysis of causal attribution.
Each sample has been annotated with crowd-sourced descriptions, as well as
familiarity, imageability, arousal, and valence ratings. We extend existing
calculations of causal uncertainty, automating and generalizing them with word
embeddings. Upon analysis we find that individuals will provide less polarized
emotion ratings as a sound's source becomes increasingly ambiguous; individual
ratings of familiarity and imageability, on the other hand, diverge as
uncertainty increases despite a clear negative trend on average
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