8 citations · 21 across the 11 of their papers we have counts for
12 papers
A Non-topological Extension of Bending-immune Valley Topological Edge States
Tianyuan Liu, Wei Yan, Min Qiu
Breaking parity (P) symmetry in C symmetric crystals is a common routine to implement a valley-topological phase. At an interface between two crystals of opposite valley phases…
CEMSSL: Conditional Embodied Self-Supervised Learning is All You Need for High-precision Multi-solution Inverse Kinematics of Robot Arms
Qu Weiming, Liu Tianlin, Du Jiawei +1
In the field of signal processing for robotics, the inverse kinematics of robot arms presents a significant challenge due to multiple solutions caused by redundant degrees of freed…
High-Precise Robot Arm Manipulation based on Online Iterative Learning and Forward Simulation with Positioning Error Below End-Effector Physical Minimum Displacement
Qu Weiming, Liu Tianlin, Luo Dingsheng
Precision is a crucial performance indicator for robot arms, as high precision manipulation allows for a wider range of applications. Traditional methods for improving robot arm pr…
Embodied Self-Supervised Learning (EMSSL) with Sampling and Training Coordination for Robot Arm Inverse Kinematics Model Learning
Qu Weiming, Liu Tianlin, Wu Xihong +1
Forward and inverse kinematics models are fundamental to robot arms, serving as the basis for the robot arm's operational tasks. However, in model learning of robot arms, especiall…
Universal Approximation Under Constraints is Possible with Transformers
Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu +1
Many practical problems need the output of a machine learning model to satisfy a set of constraints, . Nevertheless, there is no known guarantee that classical neural network ar…
Harnessing Slow Dynamics in Neuromorphic Computation
Tianlin Liu
Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior…
Continual Learning for Sentence Representations Using Conceptors
Tianlin Liu, Lyle Ungar, João Sedoc
Distributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence represen…
Unsupervised Post-processing of Word Vectors via Conceptor Negation
Tianlin Liu, Lyle Ungar, João Sedoc
Word vectors are at the core of many natural language processing tasks. Recently, there has been interest in post-processing word vectors to enrich their semantic information. In t…
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors
Tianlin Liu, João Sedoc, Lyle Ungar
Distributed representations of words, better known as word embeddings, have become important building blocks for natural language processing tasks. Numerous studies are devoted to…
A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values
Tianlin Liu
In the traditional framework of spectral learning of stochastic time series models, model parameters are estimated based on trajectories of fully recorded observations. However, re…
Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs
Tianlin Liu, Arvid Kappas
In this paper, we describe our approach for the OMG- Emotion Challenge 2018. The goal is to produce utterance-level valence and arousal estimations for videos of approximately 1 mi…
Binary Compressive Sensing via Smoothed Gradient Descent
Tianlin Liu, Dae Gwan Lee
We present a Compressive Sensing algorithm for reconstructing binary signals from its linear measurements. The proposed algorithm minimizes a non-convex cost function expressed as…