354 research outputs found
FAMO: Fast Adaptive Multitask Optimization
One of the grand enduring goals of AI is to create generalist agents that can
learn multiple different tasks from diverse data via multitask learning (MTL).
However, gradient descent (GD) on the average loss across all tasks may yield
poor multitask performance due to severe under-optimization of certain tasks.
Previous approaches that manipulate task gradients for a more balanced loss
decrease require storing and computing all task gradients (O(K) space and time
where K is the number of tasks), limiting their use in large-scale scenarios.
In this work, we introduce Fast Adaptive Multitask Optimization (FAMO), a
dynamic weighting method that decreases task losses in a balanced way using
O(1) space and time. We conduct an extensive set of experiments covering
multi-task supervised and reinforcement learning problems. Our results indicate
that FAMO achieves comparable or superior performance to state-of-the-art
gradient manipulation techniques while offering significant improvements in
space and computational efficiency. Code is available at
https://github.com/Cranial-XIX/FAMO
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (GCRL) has a wide range of potential
real-world applications, including manipulation and navigation problems in
robotics. Especially in such robotics tasks, sample efficiency is of the utmost
importance for GCRL since, by default, the agent is only rewarded when it
reaches its goal. While several methods have been proposed to improve the
sample efficiency of GCRL, one relatively under-studied approach is the design
of neural architectures to support sample efficiency. In this work, we
introduce a novel neural architecture for GCRL that achieves significantly
better sample efficiency than the commonly-used monolithic network
architecture. The key insight is that the optimal action-value function Q^*(s,
a, g) must satisfy the triangle inequality in a specific sense. Furthermore, we
introduce the metric residual network (MRN) that deliberately decomposes the
action-value function Q(s,a,g) into the negated summation of a metric plus a
residual asymmetric component. MRN provably approximates any optimal
action-value function Q^*(s,a,g), thus making it a fitting neural architecture
for GCRL. We conduct comprehensive experiments across 12 standard benchmark
environments in GCRL. The empirical results demonstrate that MRN uniformly
outperforms other state-of-the-art GCRL neural architectures in terms of sample
efficiency.Comment: Goal-conditioned reinforcement learning, neural architecture desig
Efficient HDR Reconstruction from Real-World Raw Images
High dynamic range (HDR) imaging is still a significant yet challenging
problem due to the limited dynamic range of generic image sensors. Most
existing learning-based HDR reconstruction methods take a set of
bracketed-exposure sRGB images to extend the dynamic range, and thus are
computational- and memory-inefficient by requiring the Image Signal Processor
(ISP) to produce multiple sRGB images from the raw ones. In this paper, we
propose to broaden the dynamic range from the raw inputs and perform only one
ISP processing for the reconstructed HDR raw image. Our key insights are
threefold: (1) we design a new computational raw HDR data formation pipeline
and construct the first real-world raw HDR dataset, RealRaw-HDR; (2) we develop
a lightweight-efficient HDR model, RepUNet, using the structural
re-parameterization technique; (3) we propose a plug-and-play motion alignment
loss to mitigate motion misalignment between short- and long-exposure images.
Extensive experiments demonstrate that our approach achieves state-of-the-art
performance in both visual quality and quantitative metrics
LeCo: Lightweight Compression via Learning Serial Correlations
Lightweight data compression is a key technique that allows column stores to
exhibit superior performance for analytical queries. Despite a comprehensive
study on dictionary-based encodings to approach Shannon's entropy, few prior
works have systematically exploited the serial correlation in a column for
compression. In this paper, we propose LeCo (i.e., Learned Compression), a
framework that uses machine learning to remove the serial redundancy in a value
sequence automatically to achieve an outstanding compression ratio and
decompression performance simultaneously. LeCo presents a general approach to
this end, making existing (ad-hoc) algorithms such as Frame-of-Reference (FOR),
Delta Encoding, and Run-Length Encoding (RLE) special cases under our
framework. Our microbenchmark with three synthetic and six real-world data sets
shows that a prototype of LeCo achieves a Pareto improvement on both
compression ratio and random access speed over the existing solutions. When
integrating LeCo into widely-used applications, we observe up to 3.9x speed up
in filter-scanning a Parquet file and a 16% increase in Rocksdb's throughput
Time and job satisfaction: a longitudinal study of the differential roles of age and tenure
The relationship between job satisfaction and time is a fundamental question in organizational behavior. Yet, given inconsistent results in the literature, the nature of this relationship has remained unresolved. Scholars' understanding of this relationship has been limited because studies have generally not simultaneously considered the two primary time metrics in job satisfaction research β age and tenure β and have instead relied on cross-sectional research designs. In this study, we develop and test an empirical model to provide a more definitive answer to the question of how age and tenure relate to job satisfaction. Our analyses draw on longitudinal data from 21,670 participants spanning a total of 34 waves of data collection across 40 years in two nationally representative samples. Multilevel analyses indicate that people became less satisfied as their tenure within a given organization increased, yet as people aged β and transitioned from organization to organization β their satisfaction increased. We also found that job rewards, as exemplified by pay, mediated these relationships. We discuss empirical, theoretical and practical implications of our findings
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