The simplest way to obtain continuous interpolation between two points in
high dimensional space is to draw a line between them. While previous works
focused on the general connectivity between model parameters, we explored
linear interpolation for parameters of pre-trained models after fine-tuning.
Surprisingly, we could perform linear interpolation without a performance drop
in intermediate points for fine-tuned models. For controllable text generation,
such interpolation could be seen as moving a model towards or against the
desired text attribute (e.g., positive sentiment), which could be used as
grounds for further methods for controllable text generation without inference
speed overhead.

本文探讨 fine-tuned 模型参数的线性插值，为可控文本生成提供了可能。

参数空间中的线性插值已足够用于微调语言模型

Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models

Shape is an important physical property of natural and manmade 3D objects
that characterizes their external appearances. Understanding differences
between shapes and modeling the variability within and across shape classes,
hereinafter referred to as \emph{shape analysis}, are fundamental problems to
many applications, ranging from computer vision and computer graphics to
biology and medicine. This chapter provides an overview of some of the recent
techniques that studied the shape of 3D objects that undergo non-rigid
deformations including bending and stretching. Recent surveys that covered some
aspects such classification, retrieval, recognition, and rigid or nonrigid
registration, focused on methods that use shape descriptors. Descriptors,
however, provide abstract representations that do not enable the exploration of
shape variability. In this chapter, we focus on recent techniques that treated
the shape of 3D objects as points in some high dimensional space where paths
describe deformations. Equipping the space with a suitable metric enables the
quantification of the range of deformations of a given shape, which in turn
enables (1) comparing and classifying 3D objects based on their shape, (2)
computing smooth deformations, i.e. geodesics, between pairs of objects, and
(3) modeling and exploring continuous shape variability in a collection of 3D
models. This article surveys and classifies recent developments in this field,
outlines fundamental issues, discusses their potential applications in computer
vision and graphics, and highlights opportunities for future research. Our
primary goal is to bridge the gap between various techniques that have been
often independently proposed by different communities including mathematics and
statistics, computer vision and graphics, and medical image analysis.

本文概述了近期关于形状分析的技术，探讨了以高维空间中的点为形状的方法，借助合适的度量计算给定形状的变形范围，进而比较与分类 3D 对象、计算对象间的平滑变形并模拟与探索形状变异。提出该领域的发展方向及应用前景，涉及数学、统计学、计算机视觉与图形学、医学图像分析等领域。

非刚性三维形状分析调查

A Survey on Non-rigid 3D Shape Analysis

Manifold learning based methods have been widely used for non-linear
dimensionality reduction (NLDR). However, in many practical settings, the need
to process streaming data is a challenge for such methods, owing to the high
computational complexity involved. Moreover, most methods operate under the
assumption that the input data is sampled from a single manifold, embedded in a
high dimensional space. We propose a method for streaming NLDR when the
observed data is either sampled from multiple manifolds or irregularly sampled
from a single manifold. We show that existing NLDR methods, such as Isomap,
fail in such situations, primarily because they rely on smoothness and
continuity of the underlying manifold, which is violated in the scenarios
explored in this paper. However, the proposed algorithm is able to learn
effectively in presence of multiple, and potentially intersecting, manifolds,
while allowing for the input data to arrive as a massive stream.

本文提出了一种用于多个流形或不规则采样的单个流形的流式 NLDR 方法，解决了现有方法在存在多个流形或不规则采样的单个流形时的局限性，可以有效地从大量流数据中学习。