293 research outputs found
Rumba : a Python framework for automating large-scale recursive internet experiments on GENI and FIRE+
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inferenc
Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarization on Social Media Discourse
With the growth of online news over the past decade, empirical studies on
political discourse and news consumption have focused on the phenomenon of
filter bubbles and echo chambers. Yet recently, scholars have revealed limited
evidence around the impact of such phenomenon, leading some to argue that
partisan segregation across news audiences cannot be fully explained by online
news consumption alone and that the role of traditional legacy media may be as
salient in polarizing public discourse around current events. In this work, we
expand the scope of analysis to include both online and more traditional media
by investigating the relationship between broadcast news media language and
social media discourse. By analyzing a decade's worth of closed captions (2
million speaker turns) from CNN and Fox News along with topically corresponding
discourse from Twitter, we provide a novel framework for measuring semantic
polarization between America's two major broadcast networks to demonstrate how
semantic polarization between these outlets has evolved (Study 1), peaked
(Study 2) and influenced partisan discussions on Twitter (Study 3) across the
last decade. Our results demonstrate a sharp increase in polarization in how
topically important keywords are discussed between the two channels, especially
after 2016, with overall highest peaks occurring in 2020. The two stations
discuss identical topics in drastically distinct contexts in 2020, to the
extent that there is barely any linguistic overlap in how identical keywords
are contextually discussed. Further, we demonstrate at scale, how such partisan
division in broadcast media language significantly shapes semantic polarity
trends on Twitter (and vice-versa), empirically linking for the first time, how
online discussions are influenced by televised media.Comment: 11 pages. 3 figures and 11 table
Task and Motion Planning with Large Language Models for Object Rearrangement
Multi-object rearrangement is a crucial skill for service robots, and
commonsense reasoning is frequently needed in this process. However, achieving
commonsense arrangements requires knowledge about objects, which is hard to
transfer to robots. Large language models (LLMs) are one potential source of
this knowledge, but they do not naively capture information about plausible
physical arrangements of the world. We propose LLM-GROP, which uses prompting
to extract commonsense knowledge about semantically valid object configurations
from an LLM and instantiates them with a task and motion planner in order to
generalize to varying scene geometry. LLM-GROP allows us to go from
natural-language commands to human-aligned object rearrangement in varied
environments. Based on human evaluations, our approach achieves the highest
rating while outperforming competitive baselines in terms of success rate while
maintaining comparable cumulative action costs. Finally, we demonstrate a
practical implementation of LLM-GROP on a mobile manipulator in real-world
scenarios. Supplementary materials are available at:
https://sites.google.com/view/llm-gro
Re-parameterizing Your Optimizers rather than Architectures
The well-designed structures in neural networks reflect the prior knowledge
incorporated into the models. However, though different models have various
priors, we are used to training them with model-agnostic optimizers such as
SGD. In this paper, we propose to incorporate model-specific prior knowledge
into optimizers by modifying the gradients according to a set of model-specific
hyper-parameters. Such a methodology is referred to as Gradient
Re-parameterization, and the optimizers are named RepOptimizers. For the
extreme simplicity of model structure, we focus on a VGG-style plain model and
showcase that such a simple model trained with a RepOptimizer, which is
referred to as RepOpt-VGG, performs on par with or better than the recent
well-designed models. From a practical perspective, RepOpt-VGG is a favorable
base model because of its simple structure, high inference speed and training
efficiency. Compared to Structural Re-parameterization, which adds priors into
models via constructing extra training-time structures, RepOptimizers require
no extra forward/backward computations and solve the problem of quantization.
We hope to spark further research beyond the realms of model structure design.
The code and models are publicly available at
https://github.com/DingXiaoH/RepOptimizers.Comment: Under revie
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